id sid tid token lemma pos cana-942 1 1 communications communication NOUN cana-942 1 2 on on ADP cana-942 1 3 applied apply VERB cana-942 1 4 nonlinear nonlinear ADJ cana-942 1 5 analysis analysis NOUN cana-942 1 6 issn issn NOUN cana-942 1 7 : : PUNCT cana-942 1 8 1074 1074 NUM cana-942 1 9 - - PUNCT cana-942 1 10 133x 133x NUM cana-942 1 11 vol vol NOUN cana-942 1 12 31 31 NUM cana-942 1 13 no no NOUN cana-942 1 14 . . PUNCT cana-942 2 1 4s 4s NUM cana-942 2 2 ( ( PUNCT cana-942 2 3 2024 2024 NUM cana-942 2 4 ) ) PUNCT cana-942 2 5 448 448 NUM cana-942 2 6 https://internationalpubls.com https://internationalpubls.com X cana-942 2 7 computational computational ADJ cana-942 2 8 machine machine NOUN cana-942 2 9 learning learn VERB cana-942 2 10 analytics analytic NOUN cana-942 2 11 for for ADP cana-942 2 12 prediction prediction NOUN cana-942 2 13 of of ADP cana-942 2 14 water water NOUN cana-942 2 15 quality quality NOUN cana-942 2 16 nitya nitya PROPN cana-942 2 17 nand nand NOUN cana-942 2 18 jha1 jha1 PROPN cana-942 2 19 , , PUNCT cana-942 2 20 rohit rohit PROPN cana-942 2 21 kumar kumar PROPN cana-942 2 22 singh2 singh2 PROPN cana-942 2 23 , , PUNCT cana-942 2 24 sushila sushila PROPN cana-942 2 25 sharma3 sharma3 PROPN cana-942 2 26 , , PUNCT cana-942 2 27 abhishek abhishek PROPN cana-942 2 28 kumar4 kumar4 PROPN cana-942 3 1 1assistant 1assistant NUM cana-942 3 2 professor professor NOUN cana-942 3 3 , , PUNCT cana-942 3 4 department department NOUN cana-942 3 5 of of ADP cana-942 3 6 civil civil ADJ cana-942 3 7 engineering engineering NOUN cana-942 3 8 , , PUNCT cana-942 3 9 rashtrakavi rashtrakavi VERB cana-942 3 10 ramdhari ramdhari PROPN cana-942 3 11 singh singh PROPN cana-942 3 12 dinkar dinkar PROPN cana-942 3 13 college college PROPN cana-942 3 14 of of ADP cana-942 3 15 engineering engineering PROPN cana-942 3 16 , , PUNCT cana-942 3 17 po po NOUN cana-942 3 18 ulao ulao PROPN cana-942 3 19 , , PUNCT cana-942 3 20 singhaul singhaul NOUN cana-942 3 21 , , PUNCT cana-942 3 22 begusarai begusarai INTJ cana-942 3 23 , , PUNCT cana-942 3 24 bihar bihar PROPN cana-942 3 25 , , PUNCT cana-942 3 26 india india PROPN cana-942 3 27 , , PUNCT cana-942 3 28 pin851134 pin851134 NOUN cana-942 3 29 e e NOUN cana-942 3 30 - - NOUN cana-942 3 31 mail mail NOUN cana-942 3 32 idnitya.n.jha@gmail.com idnitya.n.jha@gmail.com X cana-942 3 33 2assistant 2assistant PROPN cana-942 3 34 professor professor NOUN cana-942 3 35 , , PUNCT cana-942 3 36 department department NOUN cana-942 3 37 of of ADP cana-942 3 38 civil civil ADJ cana-942 3 39 engineering engineering NOUN cana-942 3 40 , , PUNCT cana-942 3 41 government government NOUN cana-942 3 42 engineering engineering NOUN cana-942 3 43 college college NOUN cana-942 3 44 , , PUNCT cana-942 3 45 old old ADJ cana-942 3 46 suta suta PROPN cana-942 3 47 mill mill PROPN cana-942 3 48 factory factory NOUN cana-942 3 49 , , PUNCT cana-942 3 50 mairwa mairwa ADJ cana-942 3 51 road road NOUN cana-942 3 52 , , PUNCT cana-942 3 53 siwan siwan PROPN cana-942 3 54 , , PUNCT cana-942 3 55 bihar bihar NOUN cana-942 3 56 , , PUNCT cana-942 3 57 india.pin-841226 india.pin-841226 NUM cana-942 3 58 . . PUNCT cana-942 4 1 e e X cana-942 4 2 - - NOUN cana-942 4 3 mail mail NOUN cana-942 4 4 idrohitsingh372@gmail.com idrohitsingh372@gmail.com X cana-942 4 5 3assistant 3assistant NUM cana-942 4 6 professor professor NOUN cana-942 4 7 department department NOUN cana-942 4 8 of of ADP cana-942 4 9 civil civil ADJ cana-942 4 10 engineering engineering NOUN cana-942 4 11 gaya gaya PROPN cana-942 4 12 college college PROPN cana-942 4 13 of of ADP cana-942 4 14 engineering engineering PROPN cana-942 4 15 , , PUNCT cana-942 4 16 gaya gaya PROPN cana-942 4 17 , , PUNCT cana-942 4 18 bihar bihar PROPN cana-942 4 19 , , PUNCT cana-942 4 20 india india PROPN cana-942 4 21 pin pin PROPN cana-942 4 22 code823003 code823003 PROPN cana-942 4 23 email email NOUN cana-942 4 24 idsushilasharma25@gmail.com idsushilasharma25@gmail.com X cana-942 4 25 4assistant 4assistant NUM cana-942 4 26 professor professor NOUN cana-942 4 27 , , PUNCT cana-942 4 28 department department NOUN cana-942 4 29 of of ADP cana-942 4 30 mechanical mechanical ADJ cana-942 4 31 engineering engineering NOUN cana-942 4 32 , , PUNCT cana-942 4 33 rashtrakavi rashtrakavi VERB cana-942 4 34 ramdhari ramdhari PROPN cana-942 4 35 singh singh PROPN cana-942 4 36 dinkar dinkar PROPN cana-942 4 37 college college PROPN cana-942 4 38 of of ADP cana-942 4 39 engineering engineering NOUN cana-942 4 40 , , PUNCT cana-942 4 41 poulao poulao NOUN cana-942 4 42 , , PUNCT cana-942 4 43 singhaul singhaul NOUN cana-942 4 44 , , PUNCT cana-942 4 45 begusarai begusarai INTJ cana-942 4 46 , , PUNCT cana-942 4 47 bihar bihar PROPN cana-942 4 48 , , PUNCT cana-942 4 49 india india PROPN cana-942 4 50 , , PUNCT cana-942 4 51 pin851134 pin851134 NOUN cana-942 4 52 e e NOUN cana-942 4 53 mail mail NOUN cana-942 4 54 idabhishekme10107@gmail.com idabhishekme10107@gmail.com X cana-942 4 55 article article NOUN cana-942 4 56 history history NOUN cana-942 4 57 : : PUNCT cana-942 4 58 received receive VERB cana-942 4 59 : : PUNCT cana-942 4 60 15 15 NUM cana-942 4 61 - - PUNCT cana-942 4 62 04 04 NUM cana-942 4 63 - - PUNCT cana-942 4 64 2024 2024 NUM cana-942 4 65 revised revise VERB cana-942 4 66 : : PUNCT cana-942 4 67 12 12 NUM cana-942 4 68 - - PUNCT cana-942 4 69 06 06 NUM cana-942 4 70 - - PUNCT cana-942 4 71 2024 2024 NUM cana-942 4 72 accepted accept VERB cana-942 4 73 : : PUNCT cana-942 4 74 25 25 NUM cana-942 4 75 - - PUNCT cana-942 4 76 06 06 NUM cana-942 4 77 - - PUNCT cana-942 4 78 2024 2024 NUM cana-942 4 79 abstract abstract NOUN cana-942 4 80 : : PUNCT cana-942 4 81 in in ADP cana-942 4 82 terms term NOUN cana-942 4 83 of of ADP cana-942 4 84 impacts impact NOUN cana-942 4 85 on on ADP cana-942 4 86 ecosystems ecosystem NOUN cana-942 4 87 , , PUNCT cana-942 4 88 industry industry NOUN cana-942 4 89 , , PUNCT cana-942 4 90 people people NOUN cana-942 4 91 , , PUNCT cana-942 4 92 and and CCONJ cana-942 4 93 flora flora NOUN cana-942 4 94 and and CCONJ cana-942 4 95 fauna fauna NOUN cana-942 4 96 , , PUNCT cana-942 4 97 water water NOUN cana-942 4 98 quality quality NOUN cana-942 4 99 is be AUX cana-942 4 100 paramount paramount ADJ cana-942 4 101 . . PUNCT cana-942 5 1 contamination contamination NOUN cana-942 5 2 and and CCONJ cana-942 5 3 pollution pollution NOUN cana-942 5 4 have have AUX cana-942 5 5 degraded degrade VERB cana-942 5 6 water water NOUN cana-942 5 7 quality quality NOUN cana-942 5 8 in in ADP cana-942 5 9 recent recent ADJ cana-942 5 10 decades decade NOUN cana-942 5 11 . . PUNCT cana-942 6 1 predicting predict VERB cana-942 6 2 wqc wqc NOUN cana-942 6 3 and and CCONJ cana-942 6 4 water water NOUN cana-942 6 5 quality quality NOUN cana-942 6 6 index index NOUN cana-942 6 7 ( ( PUNCT cana-942 6 8 wqi wqi VERB cana-942 6 9 ) ) PUNCT cana-942 6 10 is be AUX cana-942 6 11 the the DET cana-942 6 12 problem problem NOUN cana-942 6 13 of of ADP cana-942 6 14 this this DET cana-942 6 15 article article NOUN cana-942 6 16 ; ; PUNCT cana-942 6 17 wqi wqi VERB cana-942 6 18 is be AUX cana-942 6 19 an an DET cana-942 6 20 important important ADJ cana-942 6 21 measure measure NOUN cana-942 6 22 of of ADP cana-942 6 23 water water NOUN cana-942 6 24 validity validity NOUN cana-942 6 25 . . PUNCT cana-942 7 1 this this DET cana-942 7 2 research research NOUN cana-942 7 3 use use NOUN cana-942 7 4 machine machine NOUN cana-942 7 5 learning learn VERB cana-942 7 6 approaches approach NOUN cana-942 7 7 to to PART cana-942 7 8 forecast forecast VERB cana-942 7 9 wqi wqi NOUN cana-942 7 10 and and CCONJ cana-942 7 11 wqc wqc NOUN cana-942 7 12 , , PUNCT cana-942 7 13 and and CCONJ cana-942 7 14 it it PRON cana-942 7 15 does do VERB cana-942 7 16 so so ADV cana-942 7 17 by by ADP cana-942 7 18 optimizing optimize VERB cana-942 7 19 and and CCONJ cana-942 7 20 tweaking tweak VERB cana-942 7 21 the the DET cana-942 7 22 parameters parameter NOUN cana-942 7 23 of of ADP cana-942 7 24 several several ADJ cana-942 7 25 machine machine NOUN cana-942 7 26 learning learning NOUN cana-942 7 27 models model NOUN cana-942 7 28 . . PUNCT cana-942 8 1 parameter parameter NOUN cana-942 8 2 optimization optimization NOUN cana-942 8 3 and and CCONJ cana-942 8 4 tuning tune VERB cana-942 8 5 for for ADP cana-942 8 6 four four NUM cana-942 8 7 classification classification NOUN cana-942 8 8 models model NOUN cana-942 8 9 and and CCONJ cana-942 8 10 four four NUM cana-942 8 11 regression regression NOUN cana-942 8 12 models model NOUN cana-942 8 13 both both CCONJ cana-942 8 14 make make VERB cana-942 8 15 use use NOUN cana-942 8 16 of of ADP cana-942 8 17 grid grid NOUN cana-942 8 18 search search NOUN cana-942 8 19 , , PUNCT cana-942 8 20 an an DET cana-942 8 21 essential essential ADJ cana-942 8 22 tool tool NOUN cana-942 8 23 in in ADP cana-942 8 24 both both DET cana-942 8 25 contexts context NOUN cana-942 8 26 . . PUNCT cana-942 9 1 to to PART cana-942 9 2 forecast forecast VERB cana-942 9 3 wqc wqc NOUN cana-942 9 4 , , PUNCT cana-942 9 5 classification classification NOUN cana-942 9 6 models model NOUN cana-942 9 7 such such ADJ cana-942 9 8 as as ADP cana-942 9 9 random random ADJ cana-942 9 10 forest forest NOUN cana-942 9 11 ( ( PUNCT cana-942 9 12 rf rf NOUN cana-942 9 13 ) ) PUNCT cana-942 9 14 , , PUNCT cana-942 9 15 extreme extreme ADJ cana-942 9 16 gradient gradient NOUN cana-942 9 17 boosting boost VERB cana-942 9 18 ( ( PUNCT cana-942 9 19 xgboost xgboost ADV cana-942 9 20 ) ) PUNCT cana-942 9 21 , , PUNCT cana-942 9 22 gradient gradient NOUN cana-942 9 23 boosting boosting NOUN cana-942 9 24 ( ( PUNCT cana-942 9 25 gb gb NOUN cana-942 9 26 ) ) PUNCT cana-942 9 27 , , PUNCT cana-942 9 28 and and CCONJ cana-942 9 29 adaptive adaptive ADJ cana-942 9 30 boosting boosting NOUN cana-942 9 31 ( ( PUNCT cana-942 9 32 ada ada NOUN cana-942 9 33 - - PUNCT cana-942 9 34 boost boost NOUN cana-942 9 35 ) ) PUNCT cana-942 9 36 are be AUX cana-942 9 37 used use VERB cana-942 9 38 . . PUNCT cana-942 10 1 predicting predict VERB cana-942 10 2 wqi wqi NOUN cana-942 10 3 is be AUX cana-942 10 4 done do VERB cana-942 10 5 using use VERB cana-942 10 6 regression regression NOUN cana-942 10 7 models model NOUN cana-942 10 8 such such ADJ cana-942 10 9 as as ADP cana-942 10 10 k k NOUN cana-942 10 11 - - PUNCT cana-942 10 12 nearest near ADJ cana-942 10 13 neighbour neighbour NOUN cana-942 10 14 ( ( PUNCT cana-942 10 15 knn knn PROPN cana-942 10 16 ) ) PUNCT cana-942 10 17 , , PUNCT cana-942 10 18 decision decision NOUN cana-942 10 19 tree tree NOUN cana-942 10 20 ( ( PUNCT cana-942 10 21 dt dt PROPN cana-942 10 22 ) ) PUNCT cana-942 10 23 , , PUNCT cana-942 10 24 support support VERB cana-942 10 25 vector vector NOUN cana-942 10 26 regression regression NOUN cana-942 10 27 ( ( PUNCT cana-942 10 28 svr svr PROPN cana-942 10 29 ) ) PUNCT cana-942 10 30 , , PUNCT cana-942 10 31 and and CCONJ cana-942 10 32 multi multi ADJ cana-942 10 33 - - ADJ cana-942 10 34 layer layer ADJ cana-942 10 35 perceptron perceptron NOUN cana-942 10 36 ( ( PUNCT cana-942 10 37 mlp mlp PROPN cana-942 10 38 ) ) PUNCT cana-942 10 39 . . PUNCT cana-942 11 1 data datum NOUN cana-942 11 2 normalization normalization NOUN cana-942 11 3 and and CCONJ cana-942 11 4 data datum NOUN cana-942 11 5 imputation imputation NOUN cana-942 11 6 ( ( PUNCT cana-942 11 7 mean mean VERB cana-942 11 8 imputation imputation NOUN cana-942 11 9 ) ) PUNCT cana-942 11 10 were be AUX cana-942 11 11 also also ADV cana-942 11 12 executed execute VERB cana-942 11 13 as as ADP cana-942 11 14 pretreatment pretreatment NOUN cana-942 11 15 steps step NOUN cana-942 11 16 to to PART cana-942 11 17 suit suit VERB cana-942 11 18 the the DET cana-942 11 19 data datum NOUN cana-942 11 20 and and CCONJ cana-942 11 21 make make VERB cana-942 11 22 it it PRON cana-942 11 23 convenient convenient ADJ cana-942 11 24 for for ADP cana-942 11 25 any any DET cana-942 11 26 further further ADJ cana-942 11 27 processing processing NOUN cana-942 11 28 . . PUNCT cana-942 12 1 seven seven NUM cana-942 12 2 characteristics characteristic NOUN cana-942 12 3 and and CCONJ cana-942 12 4 ninety ninety NUM cana-942 12 5 - - PUNCT cana-942 12 6 one one NUM cana-942 12 7 cases case NOUN cana-942 12 8 make make VERB cana-942 12 9 up up ADP cana-942 12 10 the the DET cana-942 12 11 dataset dataset NOUN cana-942 12 12 used use VERB cana-942 12 13 for for ADP cana-942 12 14 this this DET cana-942 12 15 research research NOUN cana-942 12 16 . . PUNCT cana-942 13 1 five five NUM cana-942 13 2 evaluation evaluation NOUN cana-942 13 3 measures measure NOUN cana-942 13 4 were be AUX cana-942 13 5 calculated calculate VERB cana-942 13 6 to to PART cana-942 13 7 evaluate evaluate VERB cana-942 13 8 the the DET cana-942 13 9 classification classification NOUN cana-942 13 10 systems system NOUN cana-942 13 11 ' ' PART cana-942 13 12 effectiveness effectiveness NOUN cana-942 13 13 : : PUNCT cana-942 13 14 accuracy accuracy NOUN cana-942 13 15 , , PUNCT cana-942 13 16 recall recall NOUN cana-942 13 17 , , PUNCT cana-942 13 18 precision precision NOUN cana-942 13 19 , , PUNCT cana-942 13 20 matthews matthews PROPN cana-942 13 21 ' ' PART cana-942 13 22 correlation correlation NOUN cana-942 13 23 coefficient coefficient NOUN cana-942 13 24 ( ( PUNCT cana-942 13 25 mcc mcc NOUN cana-942 13 26 ) ) PUNCT cana-942 13 27 , , PUNCT cana-942 13 28 and and CCONJ cana-942 13 29 f1 f1 PROPN cana-942 13 30 score score NOUN cana-942 13 31 . . PUNCT cana-942 14 1 a a DET cana-942 14 2 total total NOUN cana-942 14 3 of of ADP cana-942 14 4 four four NUM cana-942 14 5 evaluation evaluation NOUN cana-942 14 6 metrics metric NOUN cana-942 14 7 were be AUX cana-942 14 8 calculated calculate VERB cana-942 14 9 to to PART cana-942 14 10 measure measure VERB cana-942 14 11 the the DET cana-942 14 12 efficacy efficacy NOUN cana-942 14 13 of of ADP cana-942 14 14 the the DET cana-942 14 15 regression regression NOUN cana-942 14 16 models model NOUN cana-942 14 17 : : PUNCT cana-942 14 18 mae mae PROPN cana-942 14 19 , , PUNCT cana-942 14 20 medae medae NOUN cana-942 14 21 , , PUNCT cana-942 14 22 mse mse NOUN cana-942 14 23 , , PUNCT cana-942 14 24 and and CCONJ cana-942 14 25 r2 r2 PROPN cana-942 14 26 . . PUNCT cana-942 15 1 the the DET cana-942 15 2 results result NOUN cana-942 15 3 of of ADP cana-942 15 4 the the DET cana-942 15 5 testing testing NOUN cana-942 15 6 showed show VERB cana-942 15 7 that that SCONJ cana-942 15 8 the the DET cana-942 15 9 gb gb NOUN cana-942 15 10 model model NOUN cana-942 15 11 yielded yield VERB cana-942 15 12 the the DET cana-942 15 13 most most ADV cana-942 15 14 accurate accurate ADJ cana-942 15 15 predictions prediction NOUN cana-942 15 16 of of ADP cana-942 15 17 wqc wqc NOUN cana-942 15 18 values value NOUN cana-942 15 19 ( ( PUNCT cana-942 15 20 99.50 99.50 NUM cana-942 15 21 % % NOUN cana-942 15 22 ) ) PUNCT cana-942 15 23 , , PUNCT cana-942 15 24 making make VERB cana-942 15 25 it it PRON cana-942 15 26 the the DET cana-942 15 27 top top ADJ cana-942 15 28 performer performer NOUN cana-942 15 29 in in ADP cana-942 15 30 terms term NOUN cana-942 15 31 of of ADP cana-942 15 32 categorization categorization NOUN cana-942 15 33 . . PUNCT cana-942 16 1 the the DET cana-942 16 2 experimental experimental ADJ cana-942 16 3 findings finding NOUN cana-942 16 4 show show VERB cana-942 16 5 that that SCONJ cana-942 16 6 the the DET cana-942 16 7 mlp mlp PROPN cana-942 16 8 regressor regressor NOUN cana-942 16 9 model model NOUN cana-942 16 10 got get VERB cana-942 16 11 a a DET cana-942 16 12 value value NOUN cana-942 16 13 of of ADP cana-942 16 14 99.8 99.8 NUM cana-942 16 15 percent percent NOUN cana-942 16 16 r2 r2 NOUN cana-942 16 17 when when SCONJ cana-942 16 18 predicting predict VERB cana-942 16 19 wqi wqi VERB cana-942 16 20 values value NOUN cana-942 16 21 , , PUNCT cana-942 16 22 making make VERB cana-942 16 23 it it PRON cana-942 16 24 the the DET cana-942 16 25 best well ADV cana-942 16 26 performing perform VERB cana-942 16 27 model model NOUN cana-942 16 28 in in ADP cana-942 16 29 regression regression NOUN cana-942 16 30 . . PUNCT cana-942 17 1 keywords keyword NOUN cana-942 17 2 : : PUNCT cana-942 17 3 machine machine NOUN cana-942 17 4 learning learning NOUN cana-942 17 5 , , PUNCT cana-942 17 6 water water NOUN cana-942 17 7 quality quality NOUN cana-942 17 8 , , PUNCT cana-942 17 9 prediction prediction NOUN cana-942 17 10 etc etc X cana-942 17 11 . . X cana-942 17 12 communications communication NOUN cana-942 17 13 on on ADP cana-942 17 14 applied apply VERB cana-942 17 15 nonlinear nonlinear ADJ cana-942 17 16 analysis analysis NOUN cana-942 17 17 issn issn NOUN cana-942 17 18 : : PUNCT cana-942 17 19 1074 1074 NUM cana-942 17 20 - - PUNCT cana-942 17 21 133x 133x NUM cana-942 17 22 vol vol NOUN cana-942 17 23 31 31 NUM cana-942 17 24 no no NOUN cana-942 17 25 . . PUNCT cana-942 18 1 4s 4s NUM cana-942 18 2 ( ( PUNCT cana-942 18 3 2024 2024 NUM cana-942 18 4 ) ) PUNCT cana-942 18 5 449 449 NUM cana-942 18 6 https://internationalpubls.com https://internationalpubls.com X cana-942 18 7 1 1 NUM cana-942 18 8 . . PUNCT cana-942 18 9 introduction introduction NOUN cana-942 18 10 to to PART cana-942 18 11 stay stay VERB cana-942 18 12 alive alive ADJ cana-942 18 13 , , PUNCT cana-942 18 14 water water NOUN cana-942 18 15 is be AUX cana-942 18 16 an an DET cana-942 18 17 absolute absolute ADJ cana-942 18 18 must must AUX cana-942 18 19 - - PUNCT cana-942 18 20 have have AUX cana-942 18 21 for for ADP cana-942 18 22 all all DET cana-942 18 23 forms form NOUN cana-942 18 24 of of ADP cana-942 18 25 life life NOUN cana-942 18 26 on on ADP cana-942 18 27 earth earth NOUN cana-942 18 28 , , PUNCT cana-942 18 29 including include VERB cana-942 18 30 humans human NOUN cana-942 18 31 . . PUNCT cana-942 19 1 ensuring ensure VERB cana-942 19 2 sufficient sufficient ADJ cana-942 19 3 water water NOUN cana-942 19 4 quality quality NOUN cana-942 19 5 is be AUX cana-942 19 6 of of ADP cana-942 19 7 the the DET cana-942 19 8 utmost utmost ADJ cana-942 19 9 importance importance NOUN cana-942 19 10 for for ADP cana-942 19 11 the the DET cana-942 19 12 survival survival NOUN cana-942 19 13 of of ADP cana-942 19 14 these these DET cana-942 19 15 creatures creature NOUN cana-942 19 16 . . PUNCT cana-942 20 1 excessive excessive ADJ cana-942 20 2 pollution pollution NOUN cana-942 20 3 might might AUX cana-942 20 4 have have VERB cana-942 20 5 a a DET cana-942 20 6 devastating devastating ADJ cana-942 20 7 effect effect NOUN cana-942 20 8 on on ADP cana-942 20 9 aquatic aquatic ADJ cana-942 20 10 animals animal NOUN cana-942 20 11 ' ' PART cana-942 20 12 chances chance NOUN cana-942 20 13 of of ADP cana-942 20 14 survival survival NOUN cana-942 20 15 and and CCONJ cana-942 20 16 perhaps perhaps ADV cana-942 20 17 put put VERB cana-942 20 18 their their PRON cana-942 20 19 lives life NOUN cana-942 20 20 in in ADP cana-942 20 21 jeopardy jeopardy NOUN cana-942 20 22 . . PUNCT cana-942 21 1 a a DET cana-942 21 2 variety variety NOUN cana-942 21 3 of of ADP cana-942 21 4 natural natural ADJ cana-942 21 5 water water NOUN cana-942 21 6 sources source NOUN cana-942 21 7 , , PUNCT cana-942 21 8 such such ADJ cana-942 21 9 as as ADP cana-942 21 10 rivers river NOUN cana-942 21 11 , , PUNCT cana-942 21 12 lakes lake NOUN cana-942 21 13 , , PUNCT cana-942 21 14 and and CCONJ cana-942 21 15 streams stream NOUN cana-942 21 16 , , PUNCT cana-942 21 17 may may AUX cana-942 21 18 be be AUX cana-942 21 19 evaluated evaluate VERB cana-942 21 20 according accord VERB cana-942 21 21 to to ADP cana-942 21 22 specific specific ADJ cana-942 21 23 quality quality NOUN cana-942 21 24 requirements requirement NOUN cana-942 21 25 . . PUNCT cana-942 22 1 to to PART cana-942 22 2 keep keep VERB cana-942 22 3 ecosystems ecosystem NOUN cana-942 22 4 in in ADP cana-942 22 5 their their PRON cana-942 22 6 natural natural ADJ cana-942 22 7 state state NOUN cana-942 22 8 , , PUNCT cana-942 22 9 water water NOUN cana-942 22 10 must must AUX cana-942 22 11 meet meet VERB cana-942 22 12 certain certain ADJ cana-942 22 13 criteria criterion NOUN cana-942 22 14 for for ADP cana-942 22 15 various various ADJ cana-942 22 16 uses use NOUN cana-942 22 17 ; ; PUNCT cana-942 22 18 for for ADP cana-942 22 19 example example NOUN cana-942 22 20 , , PUNCT cana-942 22 21 irrigation irrigation NOUN cana-942 22 22 water water NOUN cana-942 22 23 must must AUX cana-942 22 24 not not PART cana-942 22 25 be be AUX cana-942 22 26 too too ADV cana-942 22 27 salty salty ADJ cana-942 22 28 and and CCONJ cana-942 22 29 must must AUX cana-942 22 30 not not PART cana-942 22 31 include include VERB cana-942 22 32 pollutants pollutant NOUN cana-942 22 33 that that PRON cana-942 22 34 harm harm VERB cana-942 22 35 soil soil NOUN cana-942 22 36 and and CCONJ cana-942 22 37 plants plant NOUN cana-942 22 38 . . PUNCT cana-942 23 1 furthermore furthermore ADV cana-942 23 2 , , PUNCT cana-942 23 3 certain certain ADJ cana-942 23 4 characteristics characteristic NOUN cana-942 23 5 are be AUX cana-942 23 6 necessary necessary ADJ cana-942 23 7 for for SCONJ cana-942 23 8 industrial industrial ADJ cana-942 23 9 processes process NOUN cana-942 23 10 ' ' PART cana-942 23 11 water water NOUN cana-942 23 12 to to PART cana-942 23 13 fulfil fulfil VERB cana-942 23 14 their their PRON cana-942 23 15 specific specific ADJ cana-942 23 16 demands demand NOUN cana-942 23 17 . . PUNCT cana-942 24 1 although although SCONJ cana-942 24 2 surface surface NOUN cana-942 24 3 and and CCONJ cana-942 24 4 groundwater groundwater NOUN cana-942 24 5 are be AUX cana-942 24 6 naturally naturally ADV cana-942 24 7 occurring occur VERB cana-942 24 8 and and CCONJ cana-942 24 9 relatively relatively ADV cana-942 24 10 affordable affordable ADJ cana-942 24 11 sources source NOUN cana-942 24 12 of of ADP cana-942 24 13 freshwater freshwater NOUN cana-942 24 14 , , PUNCT cana-942 24 15 they they PRON cana-942 24 16 are be AUX cana-942 24 17 also also ADV cana-942 24 18 susceptible susceptible ADJ cana-942 24 19 to to ADP cana-942 24 20 contamination contamination NOUN cana-942 24 21 from from ADP cana-942 24 22 human human ADJ cana-942 24 23 and and CCONJ cana-942 24 24 industrial industrial ADJ cana-942 24 25 activities activity NOUN cana-942 24 26 . . PUNCT cana-942 25 1 this this PRON cana-942 25 2 is be AUX cana-942 25 3 since since SCONJ cana-942 25 4 it it PRON cana-942 25 5 supplies supply VERB cana-942 25 6 data datum NOUN cana-942 25 7 that that PRON cana-942 25 8 is be AUX cana-942 25 9 essential essential ADJ cana-942 25 10 for for ADP cana-942 25 11 managing manage VERB cana-942 25 12 and and CCONJ cana-942 25 13 protecting protect VERB cana-942 25 14 predicting predict VERB cana-942 25 15 future future ADJ cana-942 25 16 water water NOUN cana-942 25 17 quality quality NOUN cana-942 25 18 is be AUX cana-942 25 19 a a DET cana-942 25 20 major major ADJ cana-942 25 21 challenge challenge NOUN cana-942 25 22 for for ADP cana-942 25 23 environmental environmental ADJ cana-942 25 24 scientists scientist NOUN cana-942 25 25 . . PUNCT cana-942 26 1 quality quality NOUN cana-942 26 2 prediction prediction NOUN cana-942 26 3 has have AUX cana-942 26 4 been be AUX cana-942 26 5 done do VERB cana-942 26 6 using use VERB cana-942 26 7 the the DET cana-942 26 8 use use NOUN cana-942 26 9 of of ADP cana-942 26 10 physical physical ADJ cana-942 26 11 principlebased principlebase VERB cana-942 26 12 simulations simulation NOUN cana-942 26 13 or or CCONJ cana-942 26 14 empirical empirical ADJ cana-942 26 15 models model NOUN cana-942 26 16 , , PUNCT cana-942 26 17 both both PRON cana-942 26 18 of of ADP cana-942 26 19 which which PRON cana-942 26 20 may may AUX cana-942 26 21 be be AUX cana-942 26 22 resource resource NOUN cana-942 26 23 - - PUNCT cana-942 26 24 intensive intensive ADJ cana-942 26 25 and and CCONJ cana-942 26 26 timeconsuming timeconsuming ADJ cana-942 26 27 . . PUNCT cana-942 27 1 conversely conversely ADV cana-942 27 2 , , PUNCT cana-942 27 3 with with ADP cana-942 27 4 the the DET cana-942 27 5 advent advent NOUN cana-942 27 6 of of ADP cana-942 27 7 cutting cut VERB cana-942 27 8 - - PUNCT cana-942 27 9 edge edge NOUN cana-942 27 10 machine machine NOUN cana-942 27 11 learning learn VERB cana-942 27 12 techniques technique NOUN cana-942 27 13 , , PUNCT cana-942 27 14 there there PRON cana-942 27 15 has have AUX cana-942 27 16 been be AUX cana-942 27 17 a a DET cana-942 27 18 noticeable noticeable ADJ cana-942 27 19 uptick uptick NOUN cana-942 27 20 in in ADP cana-942 27 21 interest interest NOUN cana-942 27 22 in in ADP cana-942 27 23 the the DET cana-942 27 24 process process NOUN cana-942 27 25 of of ADP cana-942 27 26 creating create VERB cana-942 27 27 accurate accurate ADJ cana-942 27 28 and and CCONJ cana-942 27 29 reliable reliable ADJ cana-942 27 30 models model NOUN cana-942 27 31 for for ADP cana-942 27 32 water water NOUN cana-942 27 33 quality quality NOUN cana-942 27 34 predictions prediction NOUN cana-942 27 35 . . PUNCT cana-942 28 1 this this DET cana-942 28 2 study study NOUN cana-942 28 3 delves delve VERB cana-942 28 4 at at ADP cana-942 28 5 the the DET cana-942 28 6 potential potential NOUN cana-942 28 7 of of ADP cana-942 28 8 machine machine NOUN cana-942 28 9 learning learn VERB cana-942 28 10 algorithms algorithm NOUN cana-942 28 11 to to PART cana-942 28 12 predict predict VERB cana-942 28 13 turbidity turbidity NOUN cana-942 28 14 , , PUNCT cana-942 28 15 ph ph ADJ cana-942 28 16 , , PUNCT cana-942 28 17 and and CCONJ cana-942 28 18 dissolved dissolve VERB cana-942 28 19 oxygen oxygen NOUN cana-942 28 20 concentration concentration NOUN cana-942 28 21 β€” β€” PUNCT cana-942 28 22 indicators indicator NOUN cana-942 28 23 of of ADP cana-942 28 24 water water NOUN cana-942 28 25 quality quality NOUN cana-942 28 26 in in ADP cana-942 28 27 various various ADJ cana-942 28 28 aquatic aquatic ADJ cana-942 28 29 systems system NOUN cana-942 28 30 . . PUNCT cana-942 29 1 we we PRON cana-942 29 2 assess assess VERB cana-942 29 3 the the DET cana-942 29 4 relevant relevant ADJ cana-942 29 5 literature literature NOUN cana-942 29 6 , , PUNCT cana-942 29 7 review review VERB cana-942 29 8 the the DET cana-942 29 9 relevant relevant ADJ cana-942 29 10 research research NOUN cana-942 29 11 , , PUNCT cana-942 29 12 and and CCONJ cana-942 29 13 then then ADV cana-942 29 14 present present ADJ cana-942 29 15 case case NOUN cana-942 29 16 examples example NOUN cana-942 29 17 that that PRON cana-942 29 18 show show VERB cana-942 29 19 how how SCONJ cana-942 29 20 well well ADV cana-942 29 21 machine machine NOUN cana-942 29 22 learning learning NOUN cana-942 29 23 models model NOUN cana-942 29 24 predict predict VERB cana-942 29 25 water water NOUN cana-942 29 26 quality quality NOUN cana-942 29 27 parameters parameter NOUN cana-942 29 28 . . PUNCT cana-942 30 1 we we PRON cana-942 30 2 also also ADV cana-942 30 3 go go VERB cana-942 30 4 over over ADP cana-942 30 5 the the DET cana-942 30 6 limitations limitation NOUN cana-942 30 7 and and CCONJ cana-942 30 8 downsides downside NOUN cana-942 30 9 of of ADP cana-942 30 10 using use VERB cana-942 30 11 machine machine NOUN cana-942 30 12 learning learning NOUN cana-942 30 13 for for ADP cana-942 30 14 this this DET cana-942 30 15 purpose purpose NOUN cana-942 30 16 . . PUNCT cana-942 31 1 machine machine NOUN cana-942 31 2 learning learning NOUN cana-942 31 3 may may AUX cana-942 31 4 revolutionize revolutionize VERB cana-942 31 5 water water NOUN cana-942 31 6 quality quality NOUN cana-942 31 7 prediction prediction NOUN cana-942 31 8 , , PUNCT cana-942 31 9 according accord VERB cana-942 31 10 to to ADP cana-942 31 11 our our PRON cana-942 31 12 findings finding NOUN cana-942 31 13 , , PUNCT cana-942 31 14 paving pave VERB cana-942 31 15 the the DET cana-942 31 16 way way NOUN cana-942 31 17 for for ADP cana-942 31 18 better well ADJ cana-942 31 19 , , PUNCT cana-942 31 20 more more ADV cana-942 31 21 efficient efficient ADJ cana-942 31 22 use use NOUN cana-942 31 23 of of ADP cana-942 31 24 water water NOUN cana-942 31 25 resources resource NOUN cana-942 31 26 . . PUNCT cana-942 32 1 this this PRON cana-942 32 2 may may AUX cana-942 32 3 be be AUX cana-942 32 4 possible possible ADJ cana-942 32 5 because because SCONJ cana-942 32 6 to to PART cana-942 32 7 machine machine NOUN cana-942 32 8 learning learning NOUN cana-942 32 9 's 's PART cana-942 32 10 revolutionary revolutionary ADJ cana-942 32 11 potential potential NOUN cana-942 32 12 in in ADP cana-942 32 13 water water NOUN cana-942 32 14 quality quality NOUN cana-942 32 15 prediction prediction NOUN cana-942 32 16 . . PUNCT cana-942 33 1 2 2 X cana-942 33 2 . . X cana-942 33 3 literature literature NOUN cana-942 33 4 review review PROPN cana-942 33 5 water water NOUN cana-942 33 6 quality quality NOUN cana-942 33 7 index index NOUN cana-942 33 8 ( ( PUNCT cana-942 33 9 wqi wqi VERB cana-942 33 10 ) ) PUNCT cana-942 33 11 and and CCONJ cana-942 33 12 water water NOUN cana-942 33 13 quality quality NOUN cana-942 33 14 class class NOUN cana-942 33 15 ( ( PUNCT cana-942 33 16 wqc wqc NOUN cana-942 33 17 ) ) PUNCT cana-942 33 18 prediction prediction NOUN cana-942 33 19 using use VERB cana-942 33 20 machine machine NOUN cana-942 33 21 learning learning NOUN cana-942 33 22 algorithms algorithm NOUN cana-942 33 23 is be AUX cana-942 33 24 an an DET cana-942 33 25 expanding expand VERB cana-942 33 26 field field NOUN cana-942 33 27 of of ADP cana-942 33 28 study study NOUN cana-942 33 29 . . PUNCT cana-942 34 1 these these DET cana-942 34 2 algorithms algorithm NOUN cana-942 34 3 are be AUX cana-942 34 4 applied apply VERB cana-942 34 5 to to ADP cana-942 34 6 a a DET cana-942 34 7 variety variety NOUN cana-942 34 8 of of ADP cana-942 34 9 water water NOUN cana-942 34 10 quality quality NOUN cana-942 34 11 measures measure NOUN cana-942 34 12 , , PUNCT cana-942 34 13 including include VERB cana-942 34 14 turbidity turbidity NOUN cana-942 34 15 , , PUNCT cana-942 34 16 total total ADJ cana-942 34 17 suspended suspend VERB cana-942 34 18 solids solid NOUN cana-942 34 19 ( ( PUNCT cana-942 34 20 tss tss NOUN cana-942 34 21 ) ) PUNCT cana-942 34 22 , , PUNCT cana-942 34 23 and and CCONJ cana-942 34 24 dissolved dissolve VERB cana-942 34 25 oxygen oxygen NOUN cana-942 34 26 ( ( PUNCT cana-942 34 27 do do NOUN cana-942 34 28 ) ) PUNCT cana-942 34 29 . . PUNCT cana-942 35 1 the the DET cana-942 35 2 research research NOUN cana-942 35 3 of of ADP cana-942 35 4 this this DET cana-942 35 5 topic topic NOUN cana-942 35 6 is be AUX cana-942 35 7 called call VERB cana-942 35 8 " " PUNCT cana-942 35 9 water water NOUN cana-942 35 10 quality quality NOUN cana-942 35 11 prediction prediction NOUN cana-942 35 12 using use VERB cana-942 35 13 machine machine NOUN cana-942 35 14 learning learning NOUN cana-942 35 15 . . PUNCT cana-942 35 16 " " PUNCT cana-942 36 1 scientists scientist NOUN cana-942 36 2 have have AUX cana-942 36 3 trained train VERB cana-942 36 4 and and CCONJ cana-942 36 5 evaluated evaluate VERB cana-942 36 6 several several ADJ cana-942 36 7 algorithms algorithm NOUN cana-942 36 8 using use VERB cana-942 36 9 information information NOUN cana-942 36 10 sourced source VERB cana-942 36 11 from from ADP cana-942 36 12 a a DET cana-942 36 13 wide wide ADJ cana-942 36 14 variety variety NOUN cana-942 36 15 of of ADP cana-942 36 16 locations location NOUN cana-942 36 17 and and CCONJ cana-942 36 18 water water NOUN cana-942 36 19 sources source NOUN cana-942 36 20 . . PUNCT cana-942 37 1 the the DET cana-942 37 2 research research NOUN cana-942 37 3 paper paper NOUN cana-942 37 4 " " PUNCT cana-942 37 5 machine machine NOUN cana-942 37 6 learning learning NOUN cana-942 37 7 - - PUNCT cana-942 37 8 based base VERB cana-942 37 9 ensemble ensemble ADJ cana-942 37 10 prediction prediction NOUN cana-942 37 11 of of ADP cana-942 37 12 waterquality waterquality ADJ cana-942 37 13 variables variable NOUN cana-942 37 14 " " PUNCT cana-942 37 15 provides provide VERB cana-942 37 16 an an DET cana-942 37 17 example example NOUN cana-942 37 18 . . PUNCT cana-942 38 1 " " PUNCT cana-942 38 2 using use VERB cana-942 38 3 feature feature NOUN cana-942 38 4 - - PUNCT cana-942 38 5 level level NOUN cana-942 38 6 and and CCONJ cana-942 38 7 decision decision NOUN cana-942 38 8 - - PUNCT cana-942 38 9 level level NOUN cana-942 38 10 fusion fusion NOUN cana-942 38 11 with with ADP cana-942 38 12 proximal proximal ADJ cana-942 38 13 remote remote ADJ cana-942 38 14 sensing sensing NOUN cana-942 38 15 " " PUNCT cana-942 38 16 demonstrated demonstrate VERB cana-942 38 17 the the DET cana-942 38 18 efficacy efficacy NOUN cana-942 38 19 of of ADP cana-942 38 20 machine machine NOUN cana-942 38 21 learning learn VERB cana-942 38 22 regression regression NOUN cana-942 38 23 methods method NOUN cana-942 38 24 and and CCONJ cana-942 38 25 decision decision NOUN cana-942 38 26 - - PUNCT cana-942 38 27 level level NOUN cana-942 38 28 fusion fusion NOUN cana-942 38 29 in in ADP cana-942 38 30 predicting predict VERB cana-942 38 31 water water NOUN cana-942 38 32 - - PUNCT cana-942 38 33 quality quality NOUN cana-942 38 34 attributes attribute NOUN cana-942 38 35 using use VERB cana-942 38 36 data datum NOUN cana-942 38 37 from from ADP cana-942 38 38 three three NUM cana-942 38 39 disparate disparate ADJ cana-942 38 40 midwest midwest NOUN cana-942 38 41 bodies body NOUN cana-942 38 42 of of ADP cana-942 38 43 water water NOUN cana-942 38 44 . . PUNCT cana-942 39 1 this this DET cana-942 39 2 research research NOUN cana-942 39 3 was be AUX cana-942 39 4 titled title VERB cana-942 39 5 " " PUNCT cana-942 39 6 machine machine NOUN cana-942 39 7 learning learning NOUN cana-942 39 8 - - PUNCT cana-942 39 9 based base VERB cana-942 39 10 ensemble ensemble ADJ cana-942 39 11 prediction prediction NOUN cana-942 39 12 of of ADP cana-942 39 13 waterquality waterquality ADJ cana-942 39 14 variables variable NOUN cana-942 39 15 . . PUNCT cana-942 39 16 " " PUNCT cana-942 40 1 similarly similarly ADV cana-942 40 2 , , PUNCT cana-942 40 3 the the DET cana-942 40 4 use use NOUN cana-942 40 5 of of ADP cana-942 40 6 data datum NOUN cana-942 40 7 from from ADP cana-942 40 8 norway norway PROPN cana-942 40 9 's 's PART cana-942 40 10 brusdalsvatnet brusdalsvatnet NOUN cana-942 40 11 lake lake NOUN cana-942 40 12 in in ADP cana-942 40 13 the the DET cana-942 40 14 paper paper NOUN cana-942 40 15 " " PUNCT cana-942 40 16 emulating emulate VERB cana-942 40 17 process process NOUN cana-942 40 18 - - PUNCT cana-942 40 19 based base VERB cana-942 40 20 water water NOUN cana-942 40 21 quality quality NOUN cana-942 40 22 modeling modeling NOUN cana-942 40 23 in in ADP cana-942 40 24 water water NOUN cana-942 40 25 source source NOUN cana-942 40 26 reservoirs reservoir NOUN cana-942 40 27 using use VERB cana-942 40 28 machine machine NOUN cana-942 40 29 learning learning NOUN cana-942 40 30 " " PUNCT cana-942 40 31 demonstrates demonstrate VERB cana-942 40 32 that that SCONJ cana-942 40 33 the the DET cana-942 40 34 long long ADJ cana-942 40 35 short short ADJ cana-942 40 36 - - PUNCT cana-942 40 37 term term NOUN cana-942 40 38 memory memory NOUN cana-942 40 39 ( ( PUNCT cana-942 40 40 lstm lstm NOUN cana-942 40 41 ) ) PUNCT cana-942 40 42 model model NOUN cana-942 40 43 , , PUNCT cana-942 40 44 a a DET cana-942 40 45 subset subset NOUN cana-942 40 46 of of ADP cana-942 40 47 machine machine NOUN cana-942 40 48 learning learning NOUN cana-942 40 49 ( ( PUNCT cana-942 40 50 ml ml NOUN cana-942 40 51 ) ) PUNCT cana-942 40 52 , , PUNCT cana-942 40 53 can can AUX cana-942 40 54 effectively effectively ADV cana-942 40 55 substitute substitute VERB cana-942 40 56 process process NOUN cana-942 40 57 - - PUNCT cana-942 40 58 based base VERB cana-942 40 59 hydrodynamic hydrodynamic ADJ cana-942 40 60 and and CCONJ cana-942 40 61 water water NOUN cana-942 40 62 quality quality NOUN cana-942 40 63 models model NOUN cana-942 40 64 for for ADP cana-942 40 65 use use NOUN cana-942 40 66 in in ADP cana-942 40 67 water water NOUN cana-942 40 68 source source NOUN cana-942 40 69 management management NOUN cana-942 40 70 . . PUNCT cana-942 41 1 these these DET cana-942 41 2 models model NOUN cana-942 41 3 were be AUX cana-942 41 4 used use VERB cana-942 41 5 to to PART cana-942 41 6 replicate replicate VERB cana-942 41 7 the the DET cana-942 41 8 lake lake NOUN cana-942 41 9 's 's PART cana-942 41 10 water water NOUN cana-942 41 11 quality quality NOUN cana-942 41 12 . . PUNCT cana-942 42 1 the the DET cana-942 42 2 research research NOUN cana-942 42 3 study study NOUN cana-942 42 4 titled title VERB cana-942 42 5 " " PUNCT cana-942 42 6 water water NOUN cana-942 42 7 quality quality NOUN cana-942 42 8 prediction prediction NOUN cana-942 42 9 using use VERB cana-942 42 10 machine machine NOUN cana-942 42 11 learning learning NOUN cana-942 42 12 " " PUNCT cana-942 42 13 used use VERB cana-942 42 14 a a DET cana-942 42 15 dataset dataset NOUN cana-942 42 16 maintained maintain VERB cana-942 42 17 communications communication NOUN cana-942 42 18 on on ADP cana-942 42 19 applied apply VERB cana-942 42 20 nonlinear nonlinear ADJ cana-942 42 21 analysis analysis NOUN cana-942 42 22 issn issn NOUN cana-942 42 23 : : PUNCT cana-942 42 24 1074 1074 NUM cana-942 42 25 - - PUNCT cana-942 42 26 133x 133x NUM cana-942 42 27 vol vol NOUN cana-942 42 28 31 31 NUM cana-942 42 29 no no NOUN cana-942 42 30 . . PUNCT cana-942 43 1 4s 4s NUM cana-942 43 2 ( ( PUNCT cana-942 43 3 2024 2024 NUM cana-942 43 4 ) ) PUNCT cana-942 43 5 450 450 NUM cana-942 43 6 https://internationalpubls.com https://internationalpubls.com X cana-942 43 7 by by ADP cana-942 43 8 the the DET cana-942 43 9 central central ADJ cana-942 43 10 pollution pollution NOUN cana-942 43 11 control control PROPN cana-942 43 12 board board PROPN cana-942 43 13 of of ADP cana-942 43 14 india india PROPN cana-942 43 15 ( ( PUNCT cana-942 43 16 cpcb cpcb NOUN cana-942 43 17 ) ) PUNCT cana-942 44 1 to to PART cana-942 44 2 evaluate evaluate VERB cana-942 44 3 the the DET cana-942 44 4 efficacy efficacy NOUN cana-942 44 5 of of ADP cana-942 44 6 several several ADJ cana-942 44 7 machine machine NOUN cana-942 44 8 learning learn VERB cana-942 44 9 algorithms algorithm NOUN cana-942 44 10 in in ADP cana-942 44 11 predicting predict VERB cana-942 44 12 water water NOUN cana-942 44 13 quality quality NOUN cana-942 44 14 . . PUNCT cana-942 45 1 based base VERB cana-942 45 2 on on ADP cana-942 45 3 data datum NOUN cana-942 45 4 from from ADP cana-942 45 5 the the DET cana-942 45 6 directorate directorate NOUN cana-942 45 7 of of ADP cana-942 45 8 water water NOUN cana-942 45 9 resources resource NOUN cana-942 45 10 ( ( PUNCT cana-942 45 11 dre dre NOUN cana-942 45 12 ) ) PUNCT cana-942 45 13 of of ADP cana-942 45 14 the the DET cana-942 45 15 state state NOUN cana-942 45 16 of of ADP cana-942 45 17 illizi illizi ADJ cana-942 45 18 , , PUNCT cana-942 45 19 eight eight NUM cana-942 45 20 artificial artificial ADJ cana-942 45 21 intelligence intelligence NOUN cana-942 45 22 algorithms algorithm NOUN cana-942 45 23 were be AUX cana-942 45 24 evaluated evaluate VERB cana-942 45 25 in in ADP cana-942 45 26 the the DET cana-942 45 27 paper paper NOUN cana-942 45 28 " " PUNCT cana-942 45 29 performance performance NOUN cana-942 45 30 of of ADP cana-942 45 31 machine machine NOUN cana-942 45 32 learning learning NOUN cana-942 45 33 methods method NOUN cana-942 45 34 in in ADP cana-942 45 35 predicting predict VERB cana-942 45 36 water water NOUN cana-942 45 37 quality quality NOUN cana-942 45 38 index index NOUN cana-942 45 39 based base VERB cana-942 45 40 on on ADP cana-942 45 41 the the DET cana-942 45 42 irregular irregular ADJ cana-942 45 43 data datum NOUN cana-942 45 44 set set NOUN cana-942 45 45 : : PUNCT cana-942 45 46 application application NOUN cana-942 45 47 on on ADP cana-942 45 48 illizi illizi ADJ cana-942 45 49 region region NOUN cana-942 45 50 ( ( PUNCT cana-942 45 51 algerian algerian ADJ cana-942 45 52 southeast southeast NOUN cana-942 45 53 ) ) PUNCT cana-942 45 54 " " PUNCT cana-942 45 55 to to PART cana-942 45 56 generate generate VERB cana-942 45 57 wqi wqi ADJ cana-942 45 58 predictions prediction NOUN cana-942 45 59 in in ADP cana-942 45 60 the the DET cana-942 45 61 illizi illizi ADJ cana-942 45 62 region region NOUN cana-942 45 63 , , PUNCT cana-942 45 64 southeast southeast PROPN cana-942 45 65 algeria algeria PROPN cana-942 45 66 . . PUNCT cana-942 46 1 the the DET cana-942 46 2 data datum NOUN cana-942 46 3 was be AUX cana-942 46 4 supplied supply VERB cana-942 46 5 by by ADP cana-942 46 6 the the DET cana-942 46 7 directorate directorate NOUN cana-942 46 8 of of ADP cana-942 46 9 water water NOUN cana-942 46 10 resources resource NOUN cana-942 46 11 in in ADP cana-942 46 12 the the DET cana-942 46 13 state state NOUN cana-942 46 14 of of ADP cana-942 46 15 illizi illizi NOUN cana-942 46 16 . . PUNCT cana-942 47 1 3 3 X cana-942 47 2 . . NOUN cana-942 47 3 methodologies methodology NOUN cana-942 47 4 the the DET cana-942 47 5 following follow VERB cana-942 47 6 is be AUX cana-942 47 7 a a DET cana-942 47 8 rundown rundown NOUN cana-942 47 9 of of ADP cana-942 47 10 each each DET cana-942 47 11 process process NOUN cana-942 47 12 that that PRON cana-942 47 13 goes go VERB cana-942 47 14 into into ADP cana-942 47 15 the the DET cana-942 47 16 production production NOUN cana-942 47 17 of of ADP cana-942 47 18 our our PRON cana-942 47 19 model model NOUN cana-942 47 20 : : PUNCT cana-942 47 21 problem problem NOUN cana-942 47 22 identification identification NOUN cana-942 47 23 : : PUNCT cana-942 47 24 at at ADP cana-942 47 25 this this DET cana-942 47 26 juncture juncture NOUN cana-942 47 27 , , PUNCT cana-942 47 28 the the DET cana-942 47 29 objective objective NOUN cana-942 47 30 is be AUX cana-942 47 31 to to PART cana-942 47 32 find find VERB cana-942 47 33 the the DET cana-942 47 34 issue issue NOUN cana-942 47 35 statement statement NOUN cana-942 47 36 . . PUNCT cana-942 48 1 the the DET cana-942 48 2 task task NOUN cana-942 48 3 at at ADP cana-942 48 4 hand hand NOUN cana-942 48 5 is be AUX cana-942 48 6 to to PART cana-942 48 7 use use VERB cana-942 48 8 machine machine NOUN cana-942 48 9 learning learn VERB cana-942 48 10 to to PART cana-942 48 11 forecast forecast VERB cana-942 48 12 water water NOUN cana-942 48 13 quality quality NOUN cana-942 48 14 . . PUNCT cana-942 49 1 fig fig NOUN cana-942 49 2 1 1 NUM cana-942 49 3 . . PUNCT cana-942 49 4 algorithm algorithm NOUN cana-942 49 5 for for ADP cana-942 49 6 water water NOUN cana-942 49 7 quality quality NOUN cana-942 49 8 classification classification NOUN cana-942 49 9 data datum NOUN cana-942 49 10 extraction extraction NOUN cana-942 49 11 : : PUNCT cana-942 49 12 to to PART cana-942 49 13 analyse analyse VERB cana-942 49 14 , , PUNCT cana-942 49 15 store store NOUN cana-942 49 16 , , PUNCT cana-942 49 17 or or CCONJ cana-942 49 18 process process NOUN cana-942 49 19 data datum NOUN cana-942 49 20 at at ADP cana-942 49 21 a a DET cana-942 49 22 separate separate ADJ cana-942 49 23 location location NOUN cana-942 49 24 , , PUNCT cana-942 49 25 data datum NOUN cana-942 49 26 extraction extraction NOUN cana-942 49 27 involves involve VERB cana-942 49 28 gathering gather VERB cana-942 49 29 information information NOUN cana-942 49 30 from from ADP cana-942 49 31 one one NUM cana-942 49 32 or or CCONJ cana-942 49 33 more more ADJ cana-942 49 34 sources source NOUN cana-942 49 35 . . PUNCT cana-942 50 1 our our PRON cana-942 50 2 data datum NOUN cana-942 50 3 was be AUX cana-942 50 4 retrieved retrieve VERB cana-942 50 5 from from ADP cana-942 50 6 the the DET cana-942 50 7 website website NOUN cana-942 50 8 in in ADP cana-942 50 9 relation relation NOUN cana-942 50 10 to to ADP cana-942 50 11 the the DET cana-942 50 12 current current ADJ cana-942 50 13 condition condition NOUN cana-942 50 14 . . PUNCT cana-942 51 1 data datum NOUN cana-942 51 2 preprocessing preprocessing NOUN cana-942 51 3 : : PUNCT cana-942 51 4 improving improve VERB cana-942 51 5 the the DET cana-942 51 6 quality quality NOUN cana-942 51 7 of of ADP cana-942 51 8 data datum NOUN cana-942 51 9 analysis analysis NOUN cana-942 51 10 is be AUX cana-942 51 11 mostly mostly ADV cana-942 51 12 dependent dependent ADJ cana-942 51 13 on on ADP cana-942 51 14 processing process VERB cana-942 51 15 the the DET cana-942 51 16 data datum NOUN cana-942 51 17 . . PUNCT cana-942 52 1 to to PART cana-942 52 2 generate generate VERB cana-942 52 3 valuable valuable ADJ cana-942 52 4 and and CCONJ cana-942 52 5 applicable applicable ADJ cana-942 52 6 information information NOUN cana-942 52 7 , , PUNCT cana-942 52 8 " " PUNCT cana-942 52 9 data data NOUN cana-942 52 10 processing processing NOUN cana-942 52 11 " " PUNCT cana-942 52 12 involves involve VERB cana-942 52 13 collecting collect VERB cana-942 52 14 and and CCONJ cana-942 52 15 transforming transform VERB cana-942 52 16 different different ADJ cana-942 52 17 parts part NOUN cana-942 52 18 of of ADP cana-942 52 19 data datum NOUN cana-942 52 20 . . PUNCT cana-942 53 1 communications communication NOUN cana-942 53 2 on on ADP cana-942 53 3 applied apply VERB cana-942 53 4 nonlinear nonlinear ADJ cana-942 53 5 analysis analysis NOUN cana-942 53 6 issn issn NOUN cana-942 53 7 : : PUNCT cana-942 53 8 1074 1074 NUM cana-942 53 9 - - PUNCT cana-942 53 10 133x 133x NUM cana-942 53 11 vol vol NOUN cana-942 53 12 31 31 NUM cana-942 53 13 no no NOUN cana-942 53 14 . . PUNCT cana-942 54 1 4s 4s NUM cana-942 54 2 ( ( PUNCT cana-942 54 3 2024 2024 NUM cana-942 54 4 ) ) PUNCT cana-942 54 5 451 451 NUM cana-942 54 6 https://internationalpubls.com https://internationalpubls.com X cana-942 54 7 dealing deal VERB cana-942 54 8 with with ADP cana-942 54 9 missing miss VERB cana-942 54 10 values value NOUN cana-942 54 11 : : PUNCT cana-942 54 12 to to PART cana-942 54 13 fill fill VERB cana-942 54 14 in in ADP cana-942 54 15 data datum NOUN cana-942 54 16 that that PRON cana-942 54 17 is be AUX cana-942 54 18 lacking lack VERB cana-942 54 19 , , PUNCT cana-942 54 20 there there PRON cana-942 54 21 are be VERB cana-942 54 22 a a DET cana-942 54 23 number number NOUN cana-942 54 24 of of ADP cana-942 54 25 options option NOUN cana-942 54 26 . . PUNCT cana-942 55 1 most most ADV cana-942 55 2 often often ADV cana-942 55 3 , , PUNCT cana-942 55 4 people people NOUN cana-942 55 5 will will AUX cana-942 55 6 use use VERB cana-942 55 7 means mean NOUN cana-942 55 8 as as ADP cana-942 55 9 a a DET cana-942 55 10 technique technique NOUN cana-942 55 11 to to PART cana-942 55 12 deal deal VERB cana-942 55 13 with with ADP cana-942 55 14 numerical numerical ADJ cana-942 55 15 columns column NOUN cana-942 55 16 that that PRON cana-942 55 17 have have AUX cana-942 55 18 missing miss VERB cana-942 55 19 values value NOUN cana-942 55 20 . . PUNCT cana-942 56 1 however however ADV cana-942 56 2 , , PUNCT cana-942 56 3 means mean VERB cana-942 56 4 may may AUX cana-942 56 5 not not PART cana-942 56 6 always always ADV cana-942 56 7 be be AUX cana-942 56 8 the the DET cana-942 56 9 best good ADJ cana-942 56 10 option option NOUN cana-942 56 11 when when SCONJ cana-942 56 12 dealing deal VERB cana-942 56 13 with with ADP cana-942 56 14 data datum NOUN cana-942 56 15 that that PRON cana-942 56 16 contains contain VERB cana-942 56 17 outliers outlier NOUN cana-942 56 18 . . PUNCT cana-942 57 1 therefore therefore ADV cana-942 57 2 , , PUNCT cana-942 57 3 the the DET cana-942 57 4 outliers outlier NOUN cana-942 57 5 must must AUX cana-942 57 6 be be AUX cana-942 57 7 addressed address VERB cana-942 57 8 before before SCONJ cana-942 57 9 the the DET cana-942 57 10 mean mean ADJ cana-942 57 11 replacement replacement NOUN cana-942 57 12 method method NOUN cana-942 57 13 is be AUX cana-942 57 14 used use VERB cana-942 57 15 . . PUNCT cana-942 58 1 water water NOUN cana-942 58 2 quality quality NOUN cana-942 58 3 index index NOUN cana-942 58 4 ( ( PUNCT cana-942 58 5 wqi wqi VERB cana-942 58 6 ): ): PUNCT cana-942 58 7 ” " PUNCT cana-942 58 8 β€œ " PUNCT cana-942 58 9 one one NUM cana-942 58 10 comprehensive comprehensive ADJ cana-942 58 11 metric metric NOUN cana-942 58 12 for for ADP cana-942 58 13 water water NOUN cana-942 58 14 quality quality NOUN cana-942 58 15 that that PRON cana-942 58 16 takes take VERB cana-942 58 17 all all DET cana-942 58 18 these these DET cana-942 58 19 aspects aspect NOUN cana-942 58 20 into into ADP cana-942 58 21 account account NOUN cana-942 58 22 is be AUX cana-942 58 23 the the DET cana-942 58 24 water water NOUN cana-942 58 25 quality quality NOUN cana-942 58 26 index index NOUN cana-942 58 27 ( ( PUNCT cana-942 58 28 wqi wqi NOUN cana-942 58 29 ) ) PUNCT cana-942 58 30 . . PUNCT cana-942 59 1 there there PRON cana-942 59 2 have have AUX cana-942 59 3 been be AUX cana-942 59 4 nine nine NUM cana-942 59 5 different different ADJ cana-942 59 6 parameters parameter NOUN cana-942 59 7 used use VERB cana-942 59 8 in in ADP cana-942 59 9 the the DET cana-942 59 10 past past NOUN cana-942 59 11 to to PART cana-942 59 12 determine determine VERB cana-942 59 13 the the DET cana-942 59 14 wqi wqi NOUN cana-942 59 15 . . PUNCT cana-942 60 1 when when SCONJ cana-942 60 2 trying try VERB cana-942 60 3 to to PART cana-942 60 4 determine determine VERB cana-942 60 5 the the DET cana-942 60 6 wqi wqi NOUN cana-942 60 7 in in ADP cana-942 60 8 practice practice NOUN cana-942 60 9 , , PUNCT cana-942 60 10 formula formula NOUN cana-942 60 11 ( ( PUNCT cana-942 60 12 1 1 X cana-942 60 13 ) ) PUNCT cana-942 60 14 is be AUX cana-942 60 15 usually usually ADV cana-942 60 16 used use VERB cana-942 60 17 . . PUNCT cana-942 61 1 π‘€π‘ž1 π‘€π‘ž1 ADJ cana-942 61 2 = = PUNCT cana-942 61 3 βˆ‘ βˆ‘ PUNCT cana-942 61 4 = = SYM cana-942 61 5 1 1 NUM cana-942 61 6 π‘žπ‘– π‘žπ‘– NOUN cana-942 61 7 π‘₯ π‘₯ X cana-942 61 8 𝑀𝑖 𝑀𝑖 PRON cana-942 62 1 βˆ‘π‘ βˆ‘π‘ PROPN cana-942 62 2 = = PUNCT cana-942 62 3 𝑀𝑖 𝑀𝑖 ADP cana-942 62 4 𝑖 𝑖 VERB cana-942 62 5 𝑛 𝑛 PRON cana-942 62 6 𝑖 𝑖 ADP cana-942 62 7 data data NOUN cana-942 62 8 visualization visualization NOUN cana-942 62 9 : : PUNCT cana-942 62 10 data datum NOUN cana-942 62 11 visualization visualization NOUN cana-942 62 12 refers refer VERB cana-942 62 13 to to ADP cana-942 62 14 the the DET cana-942 62 15 act act NOUN cana-942 62 16 of of ADP cana-942 62 17 presenting present VERB cana-942 62 18 data datum NOUN cana-942 62 19 visually visually ADV cana-942 62 20 to to PART cana-942 62 21 facilitate facilitate VERB cana-942 62 22 the the DET cana-942 62 23 discovery discovery NOUN cana-942 62 24 of of ADP cana-942 62 25 trends trend NOUN cana-942 62 26 , , PUNCT cana-942 62 27 patterns pattern NOUN cana-942 62 28 , , PUNCT cana-942 62 29 correlations correlation NOUN cana-942 62 30 , , PUNCT cana-942 62 31 and and CCONJ cana-942 62 32 other other ADJ cana-942 62 33 insights insight NOUN cana-942 62 34 contained contain VERB cana-942 62 35 within within ADP cana-942 62 36 the the DET cana-942 62 37 data datum NOUN cana-942 62 38 ( ( PUNCT cana-942 62 39 fig fig NOUN cana-942 62 40 . . PUNCT cana-942 63 1 2 2 NUM cana-942 63 2 ) ) PUNCT cana-942 63 3 . . PUNCT cana-942 64 1 matrix matrix NOUN cana-942 64 2 , , PUNCT cana-942 64 3 we we PRON cana-942 64 4 can can AUX cana-942 64 5 use use VERB cana-942 64 6 easily easily ADV cana-942 64 7 accessible accessible ADJ cana-942 64 8 characteristics characteristic NOUN cana-942 64 9 to to PART cana-942 64 10 discover discover VERB cana-942 64 11 trends trend NOUN cana-942 64 12 and and CCONJ cana-942 64 13 create create VERB cana-942 64 14 dependent dependent ADJ cana-942 64 15 features feature NOUN cana-942 64 16 . . PUNCT cana-942 65 1 fig fig NOUN cana-942 65 2 2 2 NUM cana-942 65 3 water water NOUN cana-942 65 4 potability potability NOUN cana-942 65 5 correlation correlation NOUN cana-942 65 6 analysis analysis NOUN cana-942 65 7 : : PUNCT cana-942 65 8 it it PRON cana-942 65 9 is be AUX cana-942 65 10 possible possible ADJ cana-942 65 11 to to PART cana-942 65 12 find find VERB cana-942 65 13 the the DET cana-942 65 14 likely likely ADJ cana-942 65 15 correlations correlation NOUN cana-942 65 16 between between ADP cana-942 65 17 many many ADJ cana-942 65 18 factors factor NOUN cana-942 65 19 with with ADP cana-942 65 20 the the DET cana-942 65 21 use use NOUN cana-942 65 22 of of ADP cana-942 65 23 a a DET cana-942 65 24 correlation correlation NOUN cana-942 65 25 matrix matrix NOUN cana-942 65 26 , , PUNCT cana-942 65 27 which which PRON cana-942 65 28 evaluates evaluate VERB cana-942 65 29 the the DET cana-942 65 30 correlation correlation NOUN cana-942 65 31 coefficients coefficient NOUN cana-942 65 32 . . PUNCT cana-942 66 1 you you PRON cana-942 66 2 can can AUX cana-942 66 3 see see VERB cana-942 66 4 every every DET cana-942 66 5 possible possible ADJ cana-942 66 6 value value NOUN cana-942 66 7 pairing pair VERB cana-942 66 8 in in ADP cana-942 66 9 the the DET cana-942 66 10 table table NOUN cana-942 66 11 . . PUNCT cana-942 67 1 looked look VERB cana-942 67 2 examined examine VERB cana-942 67 3 in in ADP cana-942 67 4 the the DET cana-942 67 5 heatmap heatmap NOUN cana-942 67 6 that that SCONJ cana-942 67 7 the the DET cana-942 67 8 correlation correlation NOUN cana-942 67 9 created create VERB cana-942 67 10 it it PRON cana-942 67 11 is be AUX cana-942 67 12 clear clear ADJ cana-942 67 13 from from ADP cana-942 67 14 looking look VERB cana-942 67 15 at at ADP cana-942 67 16 the the DET cana-942 67 17 study study NOUN cana-942 67 18 's 's PART cana-942 67 19 figure figure NOUN cana-942 67 20 3 3 NUM cana-942 67 21 that that SCONJ cana-942 67 22 the the DET cana-942 67 23 correlation correlation NOUN cana-942 67 24 between between ADP cana-942 67 25 all all DET cana-942 67 26 the the DET cana-942 67 27 characteristics characteristic NOUN cana-942 67 28 is be AUX cana-942 67 29 weak weak ADJ cana-942 67 30 . . PUNCT cana-942 68 1 consequently consequently ADV cana-942 68 2 , , PUNCT cana-942 68 3 extracting extract VERB cana-942 68 4 any any PRON cana-942 68 5 of of ADP cana-942 68 6 the the DET cana-942 68 7 dataset dataset NOUN cana-942 68 8 's 's PART cana-942 68 9 attributes attribute NOUN cana-942 68 10 is be AUX cana-942 68 11 unnecessary unnecessary ADJ cana-942 68 12 . . PUNCT cana-942 69 1 communications communication NOUN cana-942 69 2 on on ADP cana-942 69 3 applied apply VERB cana-942 69 4 nonlinear nonlinear ADJ cana-942 69 5 analysis analysis NOUN cana-942 69 6 issn issn NOUN cana-942 69 7 : : PUNCT cana-942 69 8 1074 1074 NUM cana-942 69 9 - - PUNCT cana-942 69 10 133x 133x NUM cana-942 69 11 vol vol NOUN cana-942 69 12 31 31 NUM cana-942 69 13 no no NOUN cana-942 69 14 . . PUNCT cana-942 70 1 4s 4s NUM cana-942 70 2 ( ( PUNCT cana-942 70 3 2024 2024 NUM cana-942 70 4 ) ) PUNCT cana-942 70 5 452 452 NUM cana-942 70 6 https://internationalpubls.com https://internationalpubls.com X cana-942 70 7 4 4 X cana-942 70 8 . . PUNCT cana-942 70 9 dataset dataset VERB cana-942 70 10 fig fig NOUN cana-942 70 11 3 3 NUM cana-942 70 12 heat heat NOUN cana-942 70 13 map map NOUN cana-942 70 14 visualization visualization NOUN cana-942 70 15 of of ADP cana-942 70 16 the the DET cana-942 70 17 feature feature NOUN cana-942 70 18 correlations correlation NOUN cana-942 70 19 the the DET cana-942 70 20 dataset dataset NOUN cana-942 70 21 used use VERB cana-942 70 22 for for ADP cana-942 70 23 this this DET cana-942 70 24 study study NOUN cana-942 70 25 is be AUX cana-942 70 26 available available ADJ cana-942 70 27 at at ADP cana-942 70 28 https://www.kaggle.com/datasets/anbar https://www.kaggle.com/datasets/anbar PROPN cana-942 70 29 ivan ivan PROPN cana-942 70 30 / / SYM cana-942 70 31 indianwater indianwater NOUN cana-942 70 32 - - PUNCT cana-942 70 33 quality quality NOUN cana-942 70 34 - - PUNCT cana-942 70 35 data data NOUN cana-942 70 36 . . PUNCT cana-942 71 1 several several ADJ cana-942 71 2 sites site NOUN cana-942 71 3 along along ADP cana-942 71 4 rivers river NOUN cana-942 71 5 and and CCONJ cana-942 71 6 lakes lake NOUN cana-942 71 7 in in ADP cana-942 71 8 india india PROPN cana-942 71 9 were be AUX cana-942 71 10 surveyed survey VERB cana-942 71 11 between between ADP cana-942 71 12 2015 2015 NUM cana-942 71 13 and and CCONJ cana-942 71 14 2022 2022 NUM cana-942 71 15 for for ADP cana-942 71 16 the the DET cana-942 71 17 dataset dataset NOUN cana-942 71 18 . . PUNCT cana-942 72 1 to to PART cana-942 72 2 ensure ensure VERB cana-942 72 3 the the DET cana-942 72 4 water water NOUN cana-942 72 5 is be AUX cana-942 72 6 safe safe ADJ cana-942 72 7 to to PART cana-942 72 8 drink drink VERB cana-942 72 9 , , PUNCT cana-942 72 10 the the DET cana-942 72 11 indian indian ADJ cana-942 72 12 government government NOUN cana-942 72 13 gathered gather VERB cana-942 72 14 this this DET cana-942 72 15 data datum NOUN cana-942 72 16 . . PUNCT cana-942 73 1 in in ADP cana-942 73 2 all all PRON cana-942 73 3 , , PUNCT cana-942 73 4 there there PRON cana-942 73 5 are be VERB cana-942 73 6 2024 2024 NUM cana-942 73 7 occurrences occurrence NOUN cana-942 73 8 and and CCONJ cana-942 73 9 7 7 NUM cana-942 73 10 characteristics characteristic NOUN cana-942 73 11 in in ADP cana-942 73 12 the the DET cana-942 73 13 dataset dataset NOUN cana-942 73 14 . . PUNCT cana-942 74 1 dissolved dissolve VERB cana-942 74 2 oxygen oxygen NOUN cana-942 74 3 , , PUNCT cana-942 74 4 ph ph VERB cana-942 74 5 , , PUNCT cana-942 74 6 conductivity conductivity NOUN cana-942 74 7 , , PUNCT cana-942 74 8 biological biological ADJ cana-942 74 9 oxygen oxygen NOUN cana-942 74 10 , , PUNCT cana-942 74 11 nitrate nitrate NOUN cana-942 74 12 , , PUNCT cana-942 74 13 fecal fecal ADJ cana-942 74 14 coliform coliform NOUN cana-942 74 15 , , PUNCT cana-942 74 16 and and CCONJ cana-942 74 17 total total ADJ cana-942 74 18 coliform coliform NOUN cana-942 74 19 are be AUX cana-942 74 20 included include VERB cana-942 74 21 in in ADP cana-942 74 22 the the DET cana-942 74 23 dataset dataset NOUN cana-942 74 24 . . PUNCT cana-942 75 1 characteristics characteristic NOUN cana-942 75 2 of of ADP cana-942 75 3 the the DET cana-942 75 4 data datum NOUN cana-942 75 5 set set VERB cana-942 75 6 include include VERB cana-942 75 7 an an DET cana-942 75 8 indicator indicator NOUN cana-942 75 9 of of ADP cana-942 75 10 the the DET cana-942 75 11 amount amount NOUN cana-942 75 12 of of ADP cana-942 75 13 oxygen oxygen NOUN cana-942 75 14 dissolved dissolve VERB cana-942 75 15 in in ADP cana-942 75 16 water water NOUN cana-942 75 17 , , PUNCT cana-942 75 18 which which PRON cana-942 75 19 is be AUX cana-942 75 20 necessary necessary ADJ cana-942 75 21 for for ADP cana-942 75 22 the the DET cana-942 75 23 survival survival NOUN cana-942 75 24 of of ADP cana-942 75 25 aquatic aquatic ADJ cana-942 75 26 organisms organism NOUN cana-942 75 27 , , PUNCT cana-942 75 28 is be AUX cana-942 75 29 dissolved dissolve VERB cana-942 75 30 oxygen oxygen NOUN cana-942 75 31 . . PUNCT cana-942 76 1 the the DET cana-942 76 2 ph ph ADJ cana-942 76 3 scale scale NOUN cana-942 76 4 indicates indicate VERB cana-942 76 5 the the DET cana-942 76 6 degree degree NOUN cana-942 76 7 of of ADP cana-942 76 8 acidity acidity NOUN cana-942 76 9 or or CCONJ cana-942 76 10 basicity basicity NOUN cana-942 76 11 of of ADP cana-942 76 12 water water NOUN cana-942 76 13 by by ADP cana-942 76 14 measuring measure VERB cana-942 76 15 its its PRON cana-942 76 16 acidity acidity NOUN cana-942 76 17 or or CCONJ cana-942 76 18 alkalinity alkalinity NOUN cana-942 76 19 . . PUNCT cana-942 77 1 how how SCONJ cana-942 77 2 well well ADV cana-942 77 3 it it PRON cana-942 77 4 of of ADP cana-942 77 5 water water NOUN cana-942 77 6 , , PUNCT cana-942 77 7 which which PRON cana-942 77 8 provides provide VERB cana-942 77 9 data datum NOUN cana-942 77 10 on on ADP cana-942 77 11 the the DET cana-942 77 12 presence presence NOUN cana-942 77 13 of of ADP cana-942 77 14 dissolved dissolve VERB cana-942 77 15 solids solid NOUN cana-942 77 16 and and CCONJ cana-942 77 17 assesses assess VERB cana-942 77 18 its its PRON cana-942 77 19 ability ability NOUN cana-942 77 20 to to PART cana-942 77 21 conduct conduct VERB cana-942 77 22 electrical electrical ADJ cana-942 77 23 current current NOUN cana-942 77 24 . . PUNCT cana-942 78 1 an an DET cana-942 78 2 indicator indicator NOUN cana-942 78 3 of of ADP cana-942 78 4 the the DET cana-942 78 5 level level NOUN cana-942 78 6 of of ADP cana-942 78 7 organic organic ADJ cana-942 78 8 pollution pollution NOUN cana-942 78 9 , , PUNCT cana-942 78 10 the the DET cana-942 78 11 biological biological ADJ cana-942 78 12 oxygen oxygen NOUN cana-942 78 13 demand demand NOUN cana-942 78 14 ( ( PUNCT cana-942 78 15 bod bod NOUN cana-942 78 16 ) ) PUNCT cana-942 78 17 measures measure VERB cana-942 78 18 the the DET cana-942 78 19 amount amount NOUN cana-942 78 20 of of ADP cana-942 78 21 dissolved dissolved ADJ cana-942 78 22 oxygen oxygen NOUN cana-942 78 23 that that SCONJ cana-942 78 24 microorganisms microorganism NOUN cana-942 78 25 in in ADP cana-942 78 26 water water NOUN cana-942 78 27 ingest ingest NOUN cana-942 78 28 . . PUNCT cana-942 79 1 the the DET cana-942 79 2 nitrate nitrate NOUN cana-942 79 3 , , PUNCT cana-942 79 4 which which PRON cana-942 79 5 looks look VERB cana-942 79 6 at at ADP cana-942 79 7 the the DET cana-942 79 8 concentration concentration NOUN cana-942 79 9 of of ADP cana-942 79 10 nitrate nitrate NOUN cana-942 79 11 ions ion NOUN cana-942 79 12 in in ADP cana-942 79 13 water water NOUN cana-942 79 14 , , PUNCT cana-942 79 15 which which PRON cana-942 79 16 can can AUX cana-942 79 17 be be AUX cana-942 79 18 an an DET cana-942 79 19 indication indication NOUN cana-942 79 20 of of ADP cana-942 79 21 sewage sewage NOUN cana-942 79 22 or or CCONJ cana-942 79 23 fertilizer fertilizer NOUN cana-942 79 24 contamination contamination NOUN cana-942 79 25 . . PUNCT cana-942 80 1 due due ADP cana-942 80 2 to to ADP cana-942 80 3 its its PRON cana-942 80 4 reflection reflection NOUN cana-942 80 5 of of ADP cana-942 80 6 the the DET cana-942 80 7 presence presence NOUN cana-942 80 8 of of ADP cana-942 80 9 coliform coliform NOUN cana-942 80 10 bacteria bacteria NOUN cana-942 80 11 in in ADP cana-942 80 12 the the DET cana-942 80 13 water water NOUN cana-942 80 14 , , PUNCT cana-942 80 15 fecal fecal ADJ cana-942 80 16 coliform coliform NOUN cana-942 80 17 is be AUX cana-942 80 18 a a DET cana-942 80 19 sign sign NOUN cana-942 80 20 of of ADP cana-942 80 21 faecal faecal ADJ cana-942 80 22 contamination contamination NOUN cana-942 80 23 . . PUNCT cana-942 81 1 the the DET cana-942 81 2 sum sum NOUN cana-942 81 3 of of ADP cana-942 81 4 all all DET cana-942 81 5 coliform coliform NOUN cana-942 81 6 bacteria bacteria NOUN cana-942 81 7 , , PUNCT cana-942 81 8 whether whether SCONJ cana-942 81 9 they they PRON cana-942 81 10 originate originate VERB cana-942 81 11 from from ADP cana-942 81 12 feces fece NOUN cana-942 81 13 or or CCONJ cana-942 81 14 somewhere somewhere ADV cana-942 81 15 else else ADV cana-942 81 16 , , PUNCT cana-942 81 17 is be AUX cana-942 81 18 known know VERB cana-942 81 19 as as ADP cana-942 81 20 total total ADJ cana-942 81 21 coliform coliform NOUN cana-942 81 22 . . PUNCT cana-942 82 1 to to PART cana-942 82 2 ensure ensure VERB cana-942 82 3 the the DET cana-942 82 4 dataset dataset NOUN cana-942 82 5 was be AUX cana-942 82 6 usable usable ADJ cana-942 82 7 and and CCONJ cana-942 82 8 of of ADP cana-942 82 9 high high ADJ cana-942 82 10 quality quality NOUN cana-942 82 11 for for ADP cana-942 82 12 the the DET cana-942 82 13 research research NOUN cana-942 82 14 , , PUNCT cana-942 82 15 some some DET cana-942 82 16 preprocessing preprocessing NOUN cana-942 82 17 procedures procedure NOUN cana-942 82 18 were be AUX cana-942 82 19 carried carry VERB cana-942 82 20 out out ADP cana-942 82 21 . . PUNCT cana-942 83 1 both both DET cana-942 83 2 outliers outlier NOUN cana-942 83 3 and and CCONJ cana-942 83 4 missing miss VERB cana-942 83 5 values value NOUN cana-942 83 6 are be AUX cana-942 83 7 common common ADJ cana-942 83 8 in in ADP cana-942 83 9 real real ADJ cana-942 83 10 - - PUNCT cana-942 83 11 world world NOUN cana-942 83 12 datasets dataset NOUN cana-942 83 13 , , PUNCT cana-942 83 14 and and CCONJ cana-942 83 15 both both DET cana-942 83 16 procedures procedure NOUN cana-942 83 17 must must AUX cana-942 83 18 be be AUX cana-942 83 19 addressed address VERB cana-942 83 20 . . PUNCT cana-942 84 1 there there PRON cana-942 84 2 is be VERB cana-942 84 3 a a DET cana-942 84 4 lack lack NOUN cana-942 84 5 of of ADP cana-942 84 6 information information NOUN cana-942 84 7 on on ADP cana-942 84 8 the the DET cana-942 84 9 data datum NOUN cana-942 84 10 pretreatment pretreatment NOUN cana-942 84 11 phases phase NOUN cana-942 84 12 in in ADP cana-942 84 13 the the DET cana-942 84 14 provided provide VERB cana-942 84 15 context context NOUN cana-942 84 16 . . PUNCT cana-942 85 1 furthermore furthermore ADV cana-942 85 2 , , PUNCT cana-942 85 3 statistical statistical ADJ cana-942 85 4 calculations calculation NOUN cana-942 85 5 on on ADP cana-942 85 6 the the DET cana-942 85 7 dataset dataset NOUN cana-942 85 8 properties property NOUN cana-942 85 9 were be AUX cana-942 85 10 also also ADV cana-942 85 11 a a DET cana-942 85 12 part part NOUN cana-942 85 13 of of ADP cana-942 85 14 the the DET cana-942 85 15 research research NOUN cana-942 85 16 ( ( PUNCT cana-942 85 17 table table NOUN cana-942 85 18 2 2 NUM cana-942 85 19 ) ) PUNCT cana-942 85 20 . . PUNCT cana-942 86 1 in in ADP cana-942 86 2 order order NOUN cana-942 86 3 to to PART cana-942 86 4 learn learn VERB cana-942 86 5 more more ADJ cana-942 86 6 about about ADP cana-942 86 7 the the DET cana-942 86 8 distribution distribution NOUN cana-942 86 9 and and CCONJ cana-942 86 10 characteristics characteristic NOUN cana-942 86 11 of of ADP cana-942 86 12 the the DET cana-942 86 13 data datum NOUN cana-942 86 14 , , PUNCT cana-942 86 15 these these DET cana-942 86 16 calculations calculation NOUN cana-942 86 17 may may AUX cana-942 86 18 use use VERB cana-942 86 19 metrics metric NOUN cana-942 86 20 like like ADP cana-942 86 21 the the DET cana-942 86 22 mean mean ADJ cana-942 86 23 , , PUNCT cana-942 86 24 standard standard ADJ cana-942 86 25 deviation deviation NOUN cana-942 86 26 , , PUNCT cana-942 86 27 minimum minimum ADJ cana-942 86 28 , , PUNCT cana-942 86 29 maximum maximum ADJ cana-942 86 30 , , PUNCT cana-942 86 31 and and CCONJ cana-942 86 32 quartiles quartile NOUN cana-942 86 33 . . PUNCT cana-942 87 1 in in ADP cana-942 87 2 addition addition NOUN cana-942 87 3 , , PUNCT cana-942 87 4 as as SCONJ cana-942 87 5 shown show VERB cana-942 87 6 in in ADP cana-942 87 7 figure figure NOUN cana-942 87 8 5 5 NUM cana-942 87 9 , , PUNCT cana-942 87 10 the the DET cana-942 87 11 dataset dataset NOUN cana-942 87 12 's 's PART cana-942 87 13 feature feature NOUN cana-942 87 14 correlation correlation NOUN cana-942 87 15 matrix matrix NOUN cana-942 87 16 was be AUX cana-942 87 17 examined examine VERB cana-942 87 18 . . PUNCT cana-942 88 1 to to PART cana-942 88 2 find find VERB cana-942 88 3 out out ADP cana-942 88 4 whether whether SCONJ cana-942 88 5 there there PRON cana-942 88 6 are be VERB cana-942 88 7 any any DET cana-942 88 8 major major ADJ cana-942 88 9 connections connection NOUN cana-942 88 10 or or CCONJ cana-942 88 11 dependencies dependency NOUN cana-942 88 12 between between ADP cana-942 88 13 the the DET cana-942 88 14 traits trait NOUN cana-942 88 15 , , PUNCT cana-942 88 16 the the DET cana-942 88 17 correlation correlation NOUN cana-942 88 18 matrix matrix NOUN cana-942 88 19 looks look VERB cana-942 88 20 at at ADP cana-942 88 21 how how SCONJ cana-942 88 22 they they PRON cana-942 88 23 relate relate VERB cana-942 88 24 to to ADP cana-942 88 25 one one NUM cana-942 88 26 another another DET cana-942 88 27 . . PUNCT cana-942 89 1 https://www.kaggle.com/datasets/anbarivan/indian-water-quality-data https://www.kaggle.com/datasets/anbarivan/indian-water-quality-data PROPN cana-942 89 2 https://www.kaggle.com/datasets/anbarivan/indian-water-quality-data https://www.kaggle.com/datasets/anbarivan/indian-water-quality-data PROPN cana-942 89 3 https://www.kaggle.com/datasets/anbarivan/indian-water-quality-data https://www.kaggle.com/datasets/anbarivan/indian-water-quality-data PROPN cana-942 89 4 communications communication NOUN cana-942 89 5 on on ADP cana-942 89 6 applied apply VERB cana-942 89 7 nonlinear nonlinear ADJ cana-942 89 8 analysis analysis NOUN cana-942 89 9 issn issn NOUN cana-942 89 10 : : PUNCT cana-942 89 11 1074 1074 NUM cana-942 89 12 - - PUNCT cana-942 89 13 133x 133x NUM cana-942 89 14 vol vol NOUN cana-942 89 15 31 31 NUM cana-942 89 16 no no NOUN cana-942 89 17 . . PUNCT cana-942 90 1 4s 4s NUM cana-942 90 2 ( ( PUNCT cana-942 90 3 2024 2024 NUM cana-942 90 4 ) ) PUNCT cana-942 90 5 453 453 NUM cana-942 90 6 https://internationalpubls.com https://internationalpubls.com NUM cana-942 90 7 table table NOUN cana-942 90 8 1 1 NUM cana-942 90 9 statistical statistical ADJ cana-942 90 10 calculation calculation NOUN cana-942 90 11 of of ADP cana-942 90 12 the the DET cana-942 90 13 features feature NOUN cana-942 90 14 cnt cnt PROPN cana-942 90 15 mean mean VERB cana-942 90 16 std std NOUN cana-942 90 17 min min NOUN cana-942 90 18 25 25 NUM cana-942 90 19 % % NOUN cana-942 90 20 50 50 NUM cana-942 90 21 % % NOUN cana-942 90 22 75 75 NUM cana-942 90 23 % % NOUN cana-942 90 24 max max PROPN cana-942 90 25 dissolved_oxygen dissolved_oxygen PROPN cana-942 91 1 1991 1991 NUM cana-942 91 2 6.392637 6.392637 NUM cana-942 91 3 1.322515e 1.322515e NOUN cana-942 91 4 + + CCONJ cana-942 91 5 00 00 NUM cana-942 91 6 0.0 0.0 NUM cana-942 91 7 5.95 5.95 NUM cana-942 91 8 6.70 6.70 NUM cana-942 91 9 7.2 7.2 NUM cana-942 91 10 11.4 11.4 NUM cana-942 91 11 ph ph NOUN cana-942 91 12 1991 1991 NUM cana-942 91 13 112.0906 112.0906 NUM cana-942 91 14 1.875150e 1.875150e NOUN cana-942 91 15 + + CCONJ cana-942 91 16 03 03 NUM cana-942 91 17 0.0 0.0 NUM cana-942 91 18 6.9 6.9 NUM cana-942 91 19 7.30 7.30 NUM cana-942 91 20 7.7 7.7 NUM cana-942 91 21 67115 67115 NUM cana-942 91 22 conductivity conductivity NOUN cana-942 91 23 1991 1991 NUM cana-942 91 24 1786.466 1786.466 NUM cana-942 91 25 5.517290e 5.517290e NUM cana-942 92 1 + + CCONJ cana-942 93 1 03 03 NUM cana-942 93 2 0.4 0.4 NUM cana-942 93 3 79 79 NUM cana-942 93 4 187.63 187.63 NUM cana-942 93 5 620.5 620.5 NUM cana-942 93 6 65700 65700 NUM cana-942 93 7 biological_oxygen biological_oxygen PROPN cana-942 93 8 1991 1991 NUM cana-942 93 9 6.940049 6.940049 NUM cana-942 93 10 2.908065e 2.908065e NUM cana-942 93 11 + + NUM cana-942 93 12 01 01 NUM cana-942 93 13 0.1 0.1 NUM cana-942 93 14 1.20 1.20 NUM cana-942 93 15 1.90 1.90 NUM cana-942 93 16 3.9 3.9 NUM cana-942 93 17 534.5 534.5 NUM cana-942 93 18 nitrate nitrate NOUN cana-942 93 19 1991 1991 NUM cana-942 93 20 1.623079 1.623079 NUM cana-942 93 21 3.852301e 3.852301e PROPN cana-942 94 1 + + CCONJ cana-942 94 2 00 00 NUM cana-942 95 1 0.0 0.0 NUM cana-942 95 2 0.28 0.28 NUM cana-942 95 3 0.62 0.62 NUM cana-942 95 4 1.62307 1.62307 NUM cana-942 95 5 108.7 108.7 NUM cana-942 95 6 fecal_coliform fecal_coliform NUM cana-942 95 7 1991 1991 NUM cana-942 95 8 362,529.3 362,529.3 NUM cana-942 95 9 8.038807e 8.038807e NUM cana-942 95 10 + + CCONJ cana-942 95 11 06 06 NUM cana-942 95 12 0.0 0.0 NUM cana-942 95 13 41 41 NUM cana-942 95 14 313 313 NUM cana-942 95 15 4950.5 4950.5 NUM cana-942 95 16 27252 27252 NUM cana-942 95 17 total_coliform total_coliform ADJ cana-942 95 18 1991 1991 NUM cana-942 95 19 533,687.1 533,687.1 NUM cana-942 96 1 1.375409e 1.375409e NUM cana-942 96 2 + + NOUN cana-942 96 3 07 07 NUM cana-942 96 4 0.0 0.0 NUM cana-942 96 5 118 118 NUM cana-942 96 6 542 542 NUM cana-942 96 7 2929 2929 NUM cana-942 96 8 51109 51109 NUM cana-942 96 9 wqi wqi VERB cana-942 96 10 1991 1991 NUM cana-942 96 11 75.64109 75.64109 NUM cana-942 96 12 1.359473e 1.359473e NUM cana-942 97 1 + + CCONJ cana-942 97 2 01 01 NUM cana-942 97 3 19.3 19.3 NUM cana-942 97 4 67.38 67.38 NUM cana-942 97 5 78.74 78.74 NUM cana-942 97 6 83.7 83.7 NUM cana-942 97 7 99.8 99.8 NUM cana-942 97 8 fig fig NOUN cana-942 97 9 4 4 NUM cana-942 97 10 heatmap heatmap NOUN cana-942 97 11 before before ADV cana-942 97 12 and and CCONJ cana-942 97 13 after after ADP cana-942 97 14 removing remove VERB cana-942 97 15 missing missing ADJ cana-942 97 16 values value NOUN cana-942 97 17 . . PUNCT cana-942 98 1 communications communication NOUN cana-942 98 2 on on ADP cana-942 98 3 applied apply VERB cana-942 98 4 nonlinear nonlinear ADJ cana-942 98 5 analysis analysis NOUN cana-942 98 6 issn issn NOUN cana-942 98 7 : : PUNCT cana-942 98 8 1074 1074 NUM cana-942 98 9 - - PUNCT cana-942 98 10 133x 133x NUM cana-942 98 11 vol vol NOUN cana-942 98 12 31 31 NUM cana-942 98 13 no no NOUN cana-942 98 14 . . PUNCT cana-942 99 1 4s 4s NUM cana-942 99 2 ( ( PUNCT cana-942 99 3 2024 2024 NUM cana-942 99 4 ) ) PUNCT cana-942 99 5 454 454 NUM cana-942 99 6 https://internationalpubls.com https://internationalpubls.com X cana-942 99 7 the the DET cana-942 99 8 heatmap heatmap NOUN cana-942 99 9 that that PRON cana-942 99 10 may may AUX cana-942 99 11 be be AUX cana-942 99 12 found find VERB cana-942 99 13 below below ADP cana-942 99 14 ( ( PUNCT cana-942 99 15 fig fig NOUN cana-942 99 16 . . PUNCT cana-942 99 17 4 4 X cana-942 99 18 ) ) PUNCT cana-942 99 19 displays display VERB cana-942 99 20 the the DET cana-942 99 21 correlation correlation NOUN cana-942 99 22 that that PRON cana-942 99 23 exists exist VERB cana-942 99 24 between between ADP cana-942 99 25 the the DET cana-942 99 26 various various ADJ cana-942 99 27 characteristics characteristic NOUN cana-942 99 28 . . PUNCT cana-942 100 1 data datum NOUN cana-942 100 2 splitting splitting NOUN cana-942 100 3 : : PUNCT cana-942 100 4 separating separate VERB cana-942 100 5 the the DET cana-942 100 6 data datum NOUN cana-942 100 7 into into ADP cana-942 100 8 a a DET cana-942 100 9 training training NOUN cana-942 100 10 set set NOUN cana-942 100 11 and and CCONJ cana-942 100 12 a a DET cana-942 100 13 testing testing NOUN cana-942 100 14 set set NOUN cana-942 100 15 is be AUX cana-942 100 16 an an DET cana-942 100 17 essential essential ADJ cana-942 100 18 first first ADJ cana-942 100 19 step step NOUN cana-942 100 20 in in ADP cana-942 100 21 evaluating evaluate VERB cana-942 100 22 the the DET cana-942 100 23 machine machine NOUN cana-942 100 24 learning learn VERB cana-942 100 25 model model NOUN cana-942 100 26 's 's PART cana-942 100 27 performance performance NOUN cana-942 100 28 . . PUNCT cana-942 101 1 to to PART cana-942 101 2 facilitate facilitate VERB cana-942 101 3 testing testing NOUN cana-942 101 4 and and CCONJ cana-942 101 5 training training NOUN cana-942 101 6 , , PUNCT cana-942 101 7 the the DET cana-942 101 8 dataset dataset NOUN cana-942 101 9 was be AUX cana-942 101 10 partitioned partition VERB cana-942 101 11 into into ADP cana-942 101 12 two two NUM cana-942 101 13 halves half NOUN cana-942 101 14 , , PUNCT cana-942 101 15 with with ADP cana-942 101 16 33 33 NUM cana-942 101 17 % % NOUN cana-942 101 18 of of ADP cana-942 101 19 the the DET cana-942 101 20 data datum NOUN cana-942 101 21 utilized utilize VERB cana-942 101 22 for for ADP cana-942 101 23 testing testing NOUN cana-942 101 24 and and CCONJ cana-942 101 25 67 67 NUM cana-942 101 26 % % NOUN cana-942 101 27 for for ADP cana-942 101 28 training training NOUN cana-942 101 29 . . PUNCT cana-942 102 1 for for SCONJ cana-942 102 2 the the DET cana-942 102 3 model model NOUN cana-942 102 4 to to PART cana-942 102 5 make make VERB cana-942 102 6 predictions prediction NOUN cana-942 102 7 or or CCONJ cana-942 102 8 draw draw VERB cana-942 102 9 conclusions conclusion NOUN cana-942 102 10 , , PUNCT cana-942 102 11 it it PRON cana-942 102 12 is be AUX cana-942 102 13 necessary necessary ADJ cana-942 102 14 to to PART cana-942 102 15 establish establish VERB cana-942 102 16 a a DET cana-942 102 17 relationship relationship NOUN cana-942 102 18 between between ADP cana-942 102 19 the the DET cana-942 102 20 dependent dependent ADJ cana-942 102 21 and and CCONJ cana-942 102 22 independent independent ADJ cana-942 102 23 factors factor NOUN cana-942 102 24 . . PUNCT cana-942 103 1 the the DET cana-942 103 2 results result NOUN cana-942 103 3 of of ADP cana-942 103 4 the the DET cana-942 103 5 tests test NOUN cana-942 103 6 are be AUX cana-942 103 7 then then ADV cana-942 103 8 used use VERB cana-942 103 9 to to PART cana-942 103 10 measure measure VERB cana-942 103 11 the the DET cana-942 103 12 machine machine NOUN cana-942 103 13 learning learn VERB cana-942 103 14 algorithm algorithm PROPN cana-942 103 15 's 's PART cana-942 103 16 efficacy efficacy NOUN cana-942 103 17 . . PUNCT cana-942 104 1 data datum NOUN cana-942 104 2 partitioning partitioning PROPN cana-942 104 3 allows allow VERB cana-942 104 4 it it PRON cana-942 104 5 to to PART cana-942 104 6 compute compute VERB cana-942 104 7 accuracy accuracy NOUN cana-942 104 8 metrics metric NOUN cana-942 104 9 to to PART cana-942 104 10 evaluate evaluate VERB cana-942 104 11 the the DET cana-942 104 12 model model NOUN cana-942 104 13 's 's PART cana-942 104 14 performance performance NOUN cana-942 104 15 before before ADP cana-942 104 16 applying apply VERB cana-942 104 17 it it PRON cana-942 104 18 to to ADP cana-942 104 19 real real ADJ cana-942 104 20 - - PUNCT cana-942 104 21 world world NOUN cana-942 104 22 scenario scenario NOUN cana-942 104 23 simulations simulation NOUN cana-942 104 24 . . PUNCT cana-942 105 1 5 5 X cana-942 105 2 . . X cana-942 105 3 prediction prediction NOUN cana-942 105 4 of of ADP cana-942 105 5 water water NOUN cana-942 105 6 potability potability NOUN cana-942 105 7 using use VERB cana-942 105 8 ml ml NOUN cana-942 105 9 algorithms algorithm NOUN cana-942 105 10 . . PUNCT cana-942 106 1 algorithm algorithm NOUN cana-942 106 2 : : PUNCT cana-942 106 3 the the DET cana-942 106 4 estimate estimate NOUN cana-942 106 5 of of ADP cana-942 106 6 the the DET cana-942 106 7 water water NOUN cana-942 106 8 's 's PART cana-942 106 9 potability potability NOUN cana-942 106 10 was be AUX cana-942 106 11 done do VERB cana-942 106 12 using use VERB cana-942 106 13 machine machine NOUN cana-942 106 14 learning learn VERB cana-942 106 15 algorithms algorithm NOUN cana-942 106 16 to to PART cana-942 106 17 attain attain VERB cana-942 106 18 this this DET cana-942 106 19 aim aim NOUN cana-942 106 20 . . PUNCT cana-942 107 1 we we PRON cana-942 107 2 used use VERB cana-942 107 3 algorithms algorithm NOUN cana-942 107 4 to to PART cana-942 107 5 do do VERB cana-942 107 6 both both CCONJ cana-942 107 7 the the DET cana-942 107 8 classification classification NOUN cana-942 107 9 and and CCONJ cana-942 107 10 the the DET cana-942 107 11 regression regression NOUN cana-942 107 12 . . PUNCT cana-942 108 1 several several ADJ cana-942 108 2 algorithms algorithm NOUN cana-942 108 3 were be AUX cana-942 108 4 used use VERB cana-942 108 5 during during ADP cana-942 108 6 our our PRON cana-942 108 7 research research NOUN cana-942 108 8 . . PUNCT cana-942 109 1 logistic logistic ADJ cana-942 109 2 regression regression NOUN cana-942 109 3 : : PUNCT cana-942 109 4 the the DET cana-942 109 5 goal goal NOUN cana-942 109 6 of of ADP cana-942 109 7 this this DET cana-942 109 8 regression regression NOUN cana-942 109 9 model model NOUN cana-942 109 10 is be AUX cana-942 109 11 to to PART cana-942 109 12 use use VERB cana-942 109 13 the the DET cana-942 109 14 values value NOUN cana-942 109 15 of of ADP cana-942 109 16 the the DET cana-942 109 17 independent independent ADJ cana-942 109 18 variables variable NOUN cana-942 109 19 to to PART cana-942 109 20 estimate estimate VERB cana-942 109 21 the the DET cana-942 109 22 probability probability NOUN cana-942 109 23 of of ADP cana-942 109 24 a a DET cana-942 109 25 certain certain ADJ cana-942 109 26 outcome outcome NOUN cana-942 109 27 . . PUNCT cana-942 110 1 while while SCONJ cana-942 110 2 compared compare VERB cana-942 110 3 to to ADP cana-942 110 4 linear linear PROPN cana-942 110 5 regression regression NOUN cana-942 110 6 , , PUNCT cana-942 110 7 logical logical ADJ cana-942 110 8 regression regression NOUN cana-942 110 9 considers consider VERB cana-942 110 10 the the DET cana-942 110 11 logarithm logarithm NOUN cana-942 110 12 of of ADP cana-942 110 13 the the DET cana-942 110 14 outcome outcome NOUN cana-942 110 15 variable variable NOUN cana-942 110 16 's 's PART cana-942 110 17 probability probability NOUN cana-942 110 18 while while SCONJ cana-942 110 19 making make VERB cana-942 110 20 predictions prediction NOUN cana-942 110 21 . . PUNCT cana-942 111 1 when when SCONJ cana-942 111 2 the the DET cana-942 111 3 dependent dependent ADJ cana-942 111 4 variable variable NOUN cana-942 111 5 is be AUX cana-942 111 6 continuous continuous ADJ cana-942 111 7 , , PUNCT cana-942 111 8 linear linear ADJ cana-942 111 9 regression regression NOUN cana-942 111 10 is be AUX cana-942 111 11 the the DET cana-942 111 12 method method NOUN cana-942 111 13 of of ADP cana-942 111 14 choice choice NOUN cana-942 111 15 for for ADP cana-942 111 16 data datum NOUN cana-942 111 17 analysis analysis NOUN cana-942 111 18 . . PUNCT cana-942 112 1 the the DET cana-942 112 2 dependent dependent ADJ cana-942 112 3 variable variable NOUN cana-942 112 4 may may AUX cana-942 112 5 be be AUX cana-942 112 6 described describe VERB cana-942 112 7 as as ADP cana-942 112 8 a a DET cana-942 112 9 function function NOUN cana-942 112 10 of of ADP cana-942 112 11 the the DET cana-942 112 12 independent independent ADJ cana-942 112 13 variables variable NOUN cana-942 112 14 by by ADP cana-942 112 15 this this DET cana-942 112 16 transformation transformation NOUN cana-942 112 17 , , PUNCT cana-942 112 18 but but CCONJ cana-942 112 19 its its PRON cana-942 112 20 range range NOUN cana-942 112 21 of of ADP cana-942 112 22 values value NOUN cana-942 112 23 is be AUX cana-942 112 24 restricted restrict VERB cana-942 112 25 to to ADP cana-942 112 26 just just ADV cana-942 112 27 those those PRON cana-942 112 28 between between ADP cana-942 112 29 0 0 NUM cana-942 112 30 and and CCONJ cana-942 112 31 1 1 NUM cana-942 112 32 . . X cana-942 112 33 fig fig NOUN cana-942 112 34 5 5 NUM cana-942 112 35 visualizing visualize VERB cana-942 112 36 data datum NOUN cana-942 112 37 and and CCONJ cana-942 112 38 checking check VERB cana-942 112 39 for for ADP cana-942 112 40 outliers outlier NOUN cana-942 112 41 . . PUNCT cana-942 113 1 as as SCONJ cana-942 113 2 shown show VERB cana-942 113 3 in in ADP cana-942 113 4 ( ( PUNCT cana-942 113 5 2 2 NUM cana-942 113 6 ) ) PUNCT cana-942 113 7 , , PUNCT cana-942 113 8 the the DET cana-942 113 9 sigmoid sigmoid NOUN cana-942 113 10 function function NOUN cana-942 113 11 is be AUX cana-942 113 12 utilized utilize VERB cana-942 113 13 in in ADP cana-942 113 14 the the DET cana-942 113 15 process process NOUN cana-942 113 16 of of ADP cana-942 113 17 doing do VERB cana-942 113 18 analysis analysis NOUN cana-942 113 19 in in ADP cana-942 113 20 logistic logistic ADJ cana-942 113 21 regression regression NOUN cana-942 113 22 . . PUNCT cana-942 114 1 g g NOUN cana-942 114 2 ( ( PUNCT cana-942 114 3 z z NOUN cana-942 114 4 ) ) PUNCT cana-942 114 5 = = SYM cana-942 115 1 1 1 NUM cana-942 115 2 1 1 NUM cana-942 115 3 + + NUM cana-942 115 4 𝑒^ 𝑒^ NOUN cana-942 115 5 βˆ’ βˆ’ ADP cana-942 115 6 π‘₯ π‘₯ NOUN cana-942 115 7 communications communication NOUN cana-942 115 8 on on ADP cana-942 115 9 applied apply VERB cana-942 115 10 nonlinear nonlinear ADJ cana-942 115 11 analysis analysis NOUN cana-942 115 12 issn issn NOUN cana-942 115 13 : : PUNCT cana-942 115 14 1074 1074 NUM cana-942 115 15 - - PUNCT cana-942 115 16 133x 133x NUM cana-942 115 17 vol vol NOUN cana-942 115 18 31 31 NUM cana-942 115 19 no no NOUN cana-942 115 20 . . PUNCT cana-942 116 1 4s 4s NUM cana-942 116 2 ( ( PUNCT cana-942 116 3 2024 2024 NUM cana-942 116 4 ) ) PUNCT cana-942 116 5 455 455 NUM cana-942 117 1 https://internationalpubls.com https://internationalpubls.com X cana-942 117 2 fig fig NOUN cana-942 117 3 6 6 NUM cana-942 117 4 heat heat NOUN cana-942 117 5 map map NOUN cana-942 117 6 support support NOUN cana-942 117 7 vector vector NOUN cana-942 117 8 machine machine NOUN cana-942 117 9 : : PUNCT cana-942 117 10 data data NOUN cana-942 117 11 classification classification NOUN cana-942 117 12 , , PUNCT cana-942 117 13 regression regression VERB cana-942 117 14 analysis analysis NOUN cana-942 117 15 , , PUNCT cana-942 117 16 and and CCONJ cana-942 117 17 outlier outlier NOUN cana-942 117 18 identification identification NOUN cana-942 117 19 are be AUX cana-942 117 20 all all PRON cana-942 117 21 accomplished accomplish VERB cana-942 117 22 with with ADP cana-942 117 23 the the DET cana-942 117 24 help help NOUN cana-942 117 25 of of ADP cana-942 117 26 support support NOUN cana-942 117 27 vector vector NOUN cana-942 117 28 machines machine NOUN cana-942 117 29 ( ( PUNCT cana-942 117 30 svm svm PROPN cana-942 117 31 ) ) PUNCT cana-942 117 32 . . PUNCT cana-942 118 1 in in SCONJ cana-942 118 2 order order NOUN cana-942 118 3 to to PART cana-942 118 4 effectively effectively ADV cana-942 118 5 partition partition VERB cana-942 118 6 the the DET cana-942 118 7 data datum NOUN cana-942 118 8 into into ADP cana-942 118 9 many many ADJ cana-942 118 10 groups group NOUN cana-942 118 11 , , PUNCT cana-942 118 12 support support VERB cana-942 118 13 vector vector NOUN cana-942 118 14 machines machine NOUN cana-942 118 15 ( ( PUNCT cana-942 118 16 svms svms NOUN cana-942 118 17 ) ) PUNCT cana-942 118 18 are be AUX cana-942 118 19 used use VERB cana-942 118 20 . . PUNCT cana-942 119 1 a a DET cana-942 119 2 hyperplane hyperplane NOUN cana-942 119 3 is be AUX cana-942 119 4 a a DET cana-942 119 5 plane plane NOUN cana-942 119 6 or or CCONJ cana-942 119 7 route route NOUN cana-942 119 8 that that PRON cana-942 119 9 maximizes maximize VERB cana-942 119 10 the the DET cana-942 119 11 distance distance NOUN cana-942 119 12 between between ADP cana-942 119 13 two two NUM cana-942 119 14 classes class NOUN cana-942 119 15 . . PUNCT cana-942 120 1 how how SCONJ cana-942 120 2 far far ADV cana-942 120 3 away away ADV cana-942 120 4 from from ADP cana-942 120 5 the the DET cana-942 120 6 hyperplane hyperplane NOUN cana-942 120 7 each each DET cana-942 120 8 class class NOUN cana-942 120 9 's 's PART cana-942 120 10 data datum NOUN cana-942 120 11 points point NOUN cana-942 120 12 are be AUX cana-942 120 13 from from ADP cana-942 120 14 the the DET cana-942 120 15 hyperplane hyperplane NOUN cana-942 120 16 is be AUX cana-942 120 17 what what PRON cana-942 120 18 the the DET cana-942 120 19 margin margin NOUN cana-942 120 20 is be AUX cana-942 120 21 . . PUNCT cana-942 121 1 decision decision NOUN cana-942 121 2 tree tree NOUN cana-942 121 3 classifier classifier NOUN cana-942 121 4 : : PUNCT cana-942 121 5 classification classification NOUN cana-942 121 6 problems problem NOUN cana-942 121 7 are be AUX cana-942 121 8 often often ADV cana-942 121 9 addressed address VERB cana-942 121 10 by by ADP cana-942 121 11 this this DET cana-942 121 12 kind kind NOUN cana-942 121 13 of of ADP cana-942 121 14 supervised supervised ADJ cana-942 121 15 learning learning NOUN cana-942 121 16 technique technique NOUN cana-942 121 17 , , PUNCT cana-942 121 18 which which PRON cana-942 121 19 is be AUX cana-942 121 20 extensively extensively ADV cana-942 121 21 used use VERB cana-942 121 22 in in ADP cana-942 121 23 machine machine NOUN cana-942 121 24 learning learning NOUN cana-942 121 25 . . PUNCT cana-942 122 1 the the DET cana-942 122 2 choices choice NOUN cana-942 122 3 and and CCONJ cana-942 122 4 their their PRON cana-942 122 5 related related ADJ cana-942 122 6 results result NOUN cana-942 122 7 are be AUX cana-942 122 8 represented represent VERB cana-942 122 9 in in ADP cana-942 122 10 a a DET cana-942 122 11 tree tree NOUN cana-942 122 12 - - PUNCT cana-942 122 13 like like ADJ cana-942 122 14 structure structure NOUN cana-942 122 15 . . PUNCT cana-942 123 1 every every DET cana-942 123 2 node node NOUN cana-942 123 3 in in ADP cana-942 123 4 the the DET cana-942 123 5 tree tree NOUN cana-942 123 6 stands stand VERB cana-942 123 7 for for ADP cana-942 123 8 a a DET cana-942 123 9 feature feature NOUN cana-942 123 10 , , PUNCT cana-942 123 11 and and CCONJ cana-942 123 12 the the DET cana-942 123 13 values value NOUN cana-942 123 14 linked link VERB cana-942 123 15 to to ADP cana-942 123 16 that that DET cana-942 123 17 feature feature NOUN cana-942 123 18 are be AUX cana-942 123 19 grouped group VERB cana-942 123 20 along along ADP cana-942 123 21 each each DET cana-942 123 22 branch branch NOUN cana-942 123 23 . . PUNCT cana-942 124 1 the the DET cana-942 124 2 input input NOUN cana-942 124 3 instances instance NOUN cana-942 124 4 are be AUX cana-942 124 5 represented represent VERB cana-942 124 6 by by ADP cana-942 124 7 the the DET cana-942 124 8 classes class NOUN cana-942 124 9 or or CCONJ cana-942 124 10 categories category NOUN cana-942 124 11 that that PRON cana-942 124 12 the the DET cana-942 124 13 tree tree NOUN cana-942 124 14 's 's PART cana-942 124 15 leaves leave NOUN cana-942 124 16 represent represent VERB cana-942 124 17 . . PUNCT cana-942 125 1 random random ADJ cana-942 125 2 forest forest NOUN cana-942 125 3 classifier classifier NOUN cana-942 125 4 : : PUNCT cana-942 125 5 using use VERB cana-942 125 6 a a DET cana-942 125 7 randomly randomly ADV cana-942 125 8 selected select VERB cana-942 125 9 sample sample NOUN cana-942 125 10 of of ADP cana-942 125 11 the the DET cana-942 125 12 training training NOUN cana-942 125 13 data datum NOUN cana-942 125 14 and and CCONJ cana-942 125 15 input input NOUN cana-942 125 16 attributes attribute NOUN cana-942 125 17 at at ADP cana-942 125 18 each each DET cana-942 125 19 node node NOUN cana-942 125 20 , , PUNCT cana-942 125 21 this this DET cana-942 125 22 classifier classifier NOUN cana-942 125 23 builds build VERB cana-942 125 24 a a DET cana-942 125 25 succession succession NOUN cana-942 125 26 of of ADP cana-942 125 27 decision decision NOUN cana-942 125 28 trees tree NOUN cana-942 125 29 . . PUNCT cana-942 126 1 this this PRON cana-942 126 2 improves improve VERB cana-942 126 3 the the DET cana-942 126 4 tree tree NOUN cana-942 126 5 's 's PART cana-942 126 6 ability ability NOUN cana-942 126 7 to to PART cana-942 126 8 forecast forecast VERB cana-942 126 9 future future ADJ cana-942 126 10 data datum NOUN cana-942 126 11 . . PUNCT cana-942 127 1 the the DET cana-942 127 2 model model NOUN cana-942 127 3 's 's PART cana-942 127 4 performance performance NOUN cana-942 127 5 is be AUX cana-942 127 6 enhanced enhance VERB cana-942 127 7 in in ADP cana-942 127 8 terms term NOUN cana-942 127 9 of of ADP cana-942 127 10 its its PRON cana-942 127 11 generalizability generalizability NOUN cana-942 127 12 , , PUNCT cana-942 127 13 thanks thank NOUN cana-942 127 14 to to ADP cana-942 127 15 the the DET cana-942 127 16 randomization randomization NOUN cana-942 127 17 that that PRON cana-942 127 18 helps help VERB cana-942 127 19 to to PART cana-942 127 20 minimize minimize VERB cana-942 127 21 overfitting overfitte VERB cana-942 127 22 . . PUNCT cana-942 128 1 the the DET cana-942 128 2 final final ADJ cana-942 128 3 forecast forecast NOUN cana-942 128 4 is be AUX cana-942 128 5 either either CCONJ cana-942 128 6 derived derive VERB cana-942 128 7 by by ADP cana-942 128 8 averaging average VERB cana-942 128 9 the the DET cana-942 128 10 forecasts forecast NOUN cana-942 128 11 of of ADP cana-942 128 12 all all DET cana-942 128 13 the the DET cana-942 128 14 decision decision NOUN cana-942 128 15 trees tree NOUN cana-942 128 16 in in ADP cana-942 128 17 the the DET cana-942 128 18 forest forest NOUN cana-942 128 19 or or CCONJ cana-942 128 20 by by ADP cana-942 128 21 determining determine VERB cana-942 128 22 which which DET cana-942 128 23 predictions prediction NOUN cana-942 128 24 were be AUX cana-942 128 25 most most ADV cana-942 128 26 heavily heavily ADV cana-942 128 27 voted vote VERB cana-942 128 28 . . PUNCT cana-942 129 1 each each DET cana-942 129 2 decision decision NOUN cana-942 129 3 tree tree NOUN cana-942 129 4 in in ADP cana-942 129 5 the the DET cana-942 129 6 forest forest NOUN cana-942 129 7 is be AUX cana-942 129 8 trained train VERB cana-942 129 9 independently independently ADV cana-942 129 10 . . PUNCT cana-942 130 1 xgboost xgboost PROPN cana-942 130 2 classifier classifier NOUN cana-942 130 3 : : PUNCT cana-942 130 4 the the DET cana-942 130 5 acronym acronym NOUN cana-942 130 6 xgboost xgboost ADV cana-942 130 7 stands stand VERB cana-942 130 8 for for ADP cana-942 130 9 " " PUNCT cana-942 130 10 extreme extreme ADJ cana-942 130 11 gradient gradient NOUN cana-942 130 12 boosting boosting NOUN cana-942 130 13 , , PUNCT cana-942 130 14 " " PUNCT cana-942 130 15 the the DET cana-942 130 16 name name NOUN cana-942 130 17 of of ADP cana-942 130 18 a a DET cana-942 130 19 multi multi ADJ cana-942 130 20 - - ADJ cana-942 130 21 server server ADJ cana-942 130 22 distributed distribute VERB cana-942 130 23 scalable scalable ADJ cana-942 130 24 machine machine NOUN cana-942 130 25 learning learning NOUN cana-942 130 26 algorithm algorithm NOUN cana-942 130 27 . . PUNCT cana-942 131 1 specifically specifically ADV cana-942 131 2 , , PUNCT cana-942 131 3 it it PRON cana-942 131 4 makes make VERB cana-942 131 5 use use NOUN cana-942 131 6 of of ADP cana-942 131 7 the the DET cana-942 131 8 gbdt gbdt NOUN cana-942 131 9 algorithm algorithm NOUN cana-942 131 10 . . PUNCT cana-942 132 1 some some PRON cana-942 132 2 of of ADP cana-942 132 3 the the DET cana-942 132 4 problems problem NOUN cana-942 132 5 it it PRON cana-942 132 6 can can AUX cana-942 132 7 solve solve VERB cana-942 132 8 include include VERB cana-942 132 9 regression regression NOUN cana-942 132 10 , , PUNCT cana-942 132 11 classification classification NOUN cana-942 132 12 , , PUNCT cana-942 132 13 and and CCONJ cana-942 132 14 ranking ranking NOUN cana-942 132 15 . . PUNCT cana-942 133 1 by by ADP cana-942 133 2 far far ADV cana-942 133 3 , , PUNCT cana-942 133 4 the the DET cana-942 133 5 best good ADJ cana-942 133 6 machine machine NOUN cana-942 133 7 learning learn VERB cana-942 133 8 framework framework NOUN cana-942 133 9 out out ADV cana-942 133 10 there there ADV cana-942 133 11 . . PUNCT cana-942 134 1 adaboost adaboost ADV cana-942 134 2 classifier classifier NOUN cana-942 134 3 : : PUNCT cana-942 134 4 as as ADP cana-942 134 5 part part NOUN cana-942 134 6 of of ADP cana-942 134 7 an an DET cana-942 134 8 ensemble ensemble ADJ cana-942 134 9 method method NOUN cana-942 134 10 , , PUNCT cana-942 134 11 the the DET cana-942 134 12 boosting boost VERB cana-942 134 13 strategy strategy NOUN cana-942 134 14 called call VERB cana-942 134 15 " " PUNCT cana-942 134 16 adaptive adaptive ADJ cana-942 134 17 boosting boosting NOUN cana-942 134 18 , , PUNCT cana-942 134 19 " " PUNCT cana-942 134 20 or or CCONJ cana-942 134 21 " " PUNCT cana-942 134 22 adaboost adaboost ADV cana-942 134 23 , , PUNCT cana-942 134 24 " " PUNCT cana-942 134 25 is be AUX cana-942 134 26 used use VERB cana-942 134 27 in in ADP cana-942 134 28 machine machine NOUN cana-942 134 29 learning learning NOUN cana-942 134 30 . . PUNCT cana-942 135 1 " " PUNCT cana-942 135 2 adaptive adaptive ADJ cana-942 135 3 boosting boosting NOUN cana-942 135 4 " " PUNCT cana-942 135 5 is be AUX cana-942 135 6 a a DET cana-942 135 7 possible possible ADJ cana-942 135 8 acronym acronym NOUN cana-942 135 9 communications communication NOUN cana-942 135 10 on on ADP cana-942 135 11 applied apply VERB cana-942 135 12 nonlinear nonlinear ADJ cana-942 135 13 analysis analysis NOUN cana-942 135 14 issn issn NOUN cana-942 135 15 : : PUNCT cana-942 135 16 1074 1074 NUM cana-942 135 17 - - PUNCT cana-942 135 18 133x 133x NUM cana-942 135 19 vol vol NOUN cana-942 135 20 31 31 NUM cana-942 135 21 no no NOUN cana-942 135 22 . . PUNCT cana-942 136 1 4s 4s NUM cana-942 136 2 ( ( PUNCT cana-942 136 3 2024 2024 NUM cana-942 136 4 ) ) PUNCT cana-942 136 5 456 456 NUM cana-942 136 6 https://internationalpubls.com https://internationalpubls.com X cana-942 136 7 for for ADP cana-942 136 8 " " PUNCT cana-942 136 9 adaboost adaboost ADV cana-942 136 10 . . PUNCT cana-942 136 11 " " PUNCT cana-942 137 1 it it PRON cana-942 137 2 is be AUX cana-942 137 3 called call VERB cana-942 137 4 " " PUNCT cana-942 137 5 adaptive adaptive ADJ cana-942 137 6 boosting boosting NOUN cana-942 137 7 " " PUNCT cana-942 137 8 because because SCONJ cana-942 137 9 it it PRON cana-942 137 10 reassigns reassign VERB cana-942 137 11 weights weight NOUN cana-942 137 12 to to ADP cana-942 137 13 each each DET cana-942 137 14 instance instance NOUN cana-942 137 15 , , PUNCT cana-942 137 16 giving give VERB cana-942 137 17 more more ADJ cana-942 137 18 weights weight NOUN cana-942 137 19 to to ADP cana-942 137 20 instances instance NOUN cana-942 137 21 that that PRON cana-942 137 22 were be AUX cana-942 137 23 incorrectly incorrectly ADV cana-942 137 24 categorized categorize VERB cana-942 137 25 . . PUNCT cana-942 138 1 for for ADP cana-942 138 2 the the DET cana-942 138 3 simple simple ADJ cana-942 138 4 reason reason NOUN cana-942 138 5 that that SCONJ cana-942 138 6 doing do VERB cana-942 138 7 so so ADV cana-942 138 8 has have VERB cana-942 138 9 the the DET cana-942 138 10 potential potential NOUN cana-942 138 11 to to PART cana-942 138 12 increase increase VERB cana-942 138 13 the the DET cana-942 138 14 classification classification NOUN cana-942 138 15 's 's PART cana-942 138 16 overall overall ADJ cana-942 138 17 accuracy accuracy NOUN cana-942 138 18 . . PUNCT cana-942 139 1 k k ADJ cana-942 139 2 neighbors neighbor NOUN cana-942 139 3 : : PUNCT cana-942 139 4 based base VERB cana-942 139 5 on on ADP cana-942 139 6 the the DET cana-942 139 7 principle principle NOUN cana-942 139 8 of of ADP cana-942 139 9 supervised supervised ADJ cana-942 139 10 learning learning NOUN cana-942 139 11 , , PUNCT cana-942 139 12 k k X cana-942 139 13 - - PUNCT cana-942 139 14 nearest near ADJ cana-942 139 15 neighbor neighbor NOUN cana-942 139 16 is be AUX cana-942 139 17 among among ADP cana-942 139 18 the the DET cana-942 139 19 most most ADV cana-942 139 20 fundamental fundamental ADJ cana-942 139 21 machine machine NOUN cana-942 139 22 learning learn VERB cana-942 139 23 algorithms algorithm NOUN cana-942 139 24 . . PUNCT cana-942 140 1 assuming assume VERB cana-942 140 2 the the DET cana-942 140 3 new new ADJ cana-942 140 4 instance instance NOUN cana-942 140 5 or or CCONJ cana-942 140 6 data datum NOUN cana-942 140 7 is be AUX cana-942 140 8 like like ADP cana-942 140 9 existing exist VERB cana-942 140 10 cases case NOUN cana-942 140 11 is be AUX cana-942 140 12 the the DET cana-942 140 13 operating operating NOUN cana-942 140 14 assumption assumption NOUN cana-942 140 15 under under ADP cana-942 140 16 which which PRON cana-942 140 17 the the DET cana-942 140 18 k k PROPN cana-942 140 19 - - PUNCT cana-942 140 20 nn nn ADJ cana-942 140 21 technique technique NOUN cana-942 140 22 functions function NOUN cana-942 140 23 . . PUNCT cana-942 141 1 for for ADP cana-942 141 2 each each DET cana-942 141 3 category category NOUN cana-942 141 4 , , PUNCT cana-942 141 5 a a DET cana-942 141 6 new new ADJ cana-942 141 7 instance instance NOUN cana-942 141 8 is be AUX cana-942 141 9 created create VERB cana-942 141 10 if if SCONJ cana-942 141 11 it it PRON cana-942 141 12 is be AUX cana-942 141 13 most most ADV cana-942 141 14 like like ADP cana-942 141 15 an an DET cana-942 141 16 existing exist VERB cana-942 141 17 instance instance NOUN cana-942 141 18 for for ADP cana-942 141 19 that that DET cana-942 141 20 category category NOUN cana-942 141 21 . . PUNCT cana-942 142 1 to to PART cana-942 142 2 properly properly ADV cana-942 142 3 categorize categorize VERB cana-942 142 4 new new ADJ cana-942 142 5 data datum NOUN cana-942 142 6 points point NOUN cana-942 142 7 , , PUNCT cana-942 142 8 the the DET cana-942 142 9 k k PROPN cana-942 142 10 - - PUNCT cana-942 142 11 nn nn ADJ cana-942 142 12 algorithm algorithm NOUN cana-942 142 13 must must AUX cana-942 142 14 first first ADV cana-942 142 15 recall recall VERB cana-942 142 16 all all PRON cana-942 142 17 of of ADP cana-942 142 18 the the DET cana-942 142 19 existing exist VERB cana-942 142 20 data datum NOUN cana-942 142 21 and and CCONJ cana-942 142 22 then then ADV cana-942 142 23 use use VERB cana-942 142 24 their their PRON cana-942 142 25 similarity similarity NOUN cana-942 142 26 to to ADP cana-942 142 27 previous previous ADJ cana-942 142 28 data datum NOUN cana-942 142 29 to to PART cana-942 142 30 determine determine VERB cana-942 142 31 how how SCONJ cana-942 142 32 to to PART cana-942 142 33 do do VERB cana-942 142 34 it it PRON cana-942 142 35 . . PUNCT cana-942 143 1 what what PRON cana-942 143 2 this this PRON cana-942 143 3 implies imply VERB cana-942 143 4 is be AUX cana-942 143 5 that that SCONJ cana-942 143 6 the the DET cana-942 143 7 k k PROPN cana-942 143 8 - - PUNCT cana-942 143 9 nn nn PROPN cana-942 143 10 method method NOUN cana-942 143 11 can can AUX cana-942 143 12 readily readily ADV cana-942 143 13 sort sort VERB cana-942 143 14 newly newly ADV cana-942 143 15 acquired acquire VERB cana-942 143 16 data datum NOUN cana-942 143 17 into into ADP cana-942 143 18 the the DET cana-942 143 19 correct correct ADJ cana-942 143 20 suite suite NOUN cana-942 143 21 category category NOUN cana-942 143 22 . . PUNCT cana-942 144 1 fig fig NOUN cana-942 144 2 7 7 NUM cana-942 144 3 accuracy accuracy NOUN cana-942 144 4 score score NOUN cana-942 144 5 measure measure NOUN cana-942 144 6 : : PUNCT cana-942 144 7 if if SCONJ cana-942 144 8 you you PRON cana-942 144 9 want want VERB cana-942 144 10 to to PART cana-942 144 11 know know VERB cana-942 144 12 what what PRON cana-942 144 13 factors factor NOUN cana-942 144 14 were be AUX cana-942 144 15 considered consider VERB cana-942 144 16 while while SCONJ cana-942 144 17 judging judge VERB cana-942 144 18 the the DET cana-942 144 19 model model NOUN cana-942 144 20 's 's PART cana-942 144 21 efficacy efficacy NOUN cana-942 144 22 , , PUNCT cana-942 144 23 you you PRON cana-942 144 24 may may AUX cana-942 144 25 find find VERB cana-942 144 26 them they PRON cana-942 144 27 in in ADP cana-942 144 28 the the DET cana-942 144 29 following follow VERB cana-942 144 30 list list NOUN cana-942 144 31 . . PUNCT cana-942 145 1 precision precision NOUN cana-942 145 2 : : PUNCT cana-942 145 3 is be AUX cana-942 145 4 the the DET cana-942 145 5 ratio ratio NOUN cana-942 145 6 of of ADP cana-942 145 7 the the DET cana-942 145 8 number number NOUN cana-942 145 9 of of ADP cana-942 145 10 contextual contextual ADJ cana-942 145 11 interpretations interpretation NOUN cana-942 145 12 to to ADP cana-942 145 13 the the DET cana-942 145 14 number number NOUN cana-942 145 15 of of ADP cana-942 145 16 properly properly ADV cana-942 145 17 classified classify VERB cana-942 145 18 occurrences occurrence NOUN cana-942 145 19 within within ADP cana-942 145 20 a a DET cana-942 145 21 classifier classifier NOUN cana-942 145 22 . . PUNCT cana-942 146 1 a a DET cana-942 146 2 measure measure NOUN cana-942 146 3 of of ADP cana-942 146 4 the the DET cana-942 146 5 level level NOUN cana-942 146 6 of of ADP cana-942 146 7 accuracy accuracy NOUN cana-942 146 8 associated associate VERB cana-942 146 9 with with ADP cana-942 146 10 false false ADJ cana-942 146 11 alarms alarm NOUN cana-942 146 12 is be AUX cana-942 146 13 fp fp NOUN cana-942 146 14 , , PUNCT cana-942 146 15 and and CCONJ cana-942 146 16 tp tp NOUN cana-942 146 17 , , PUNCT cana-942 146 18 an an DET cana-942 146 19 abbreviation abbreviation NOUN cana-942 146 20 for for ADP cana-942 146 21 " " PUNCT cana-942 146 22 positive positive ADJ cana-942 146 23 class class NOUN cana-942 146 24 , , PUNCT cana-942 146 25 " " PUNCT cana-942 146 26 is be AUX cana-942 146 27 calculated calculate VERB cana-942 146 28 using use VERB cana-942 146 29 equation equation NOUN cana-942 146 30 ( ( PUNCT cana-942 146 31 3 3 NUM cana-942 146 32 ) ) PUNCT cana-942 146 33 . . PUNCT cana-942 147 1 both both DET cana-942 147 2 ideas idea NOUN cana-942 147 3 pertain pertain VERB cana-942 147 4 to to PART cana-942 147 5 precision precision VERB cana-942 147 6 . . PUNCT cana-942 148 1 precision precision NOUN cana-942 148 2 = = SYM cana-942 148 3 𝑇𝑃 𝑇𝑃 NOUN cana-942 148 4 tp tp NOUN cana-942 148 5 + + CCONJ cana-942 149 1 fp fp DET cana-942 149 2 accuracy accuracy NOUN cana-942 149 3 : : PUNCT cana-942 149 4 the the DET cana-942 149 5 most most ADV cana-942 149 6 intuitive intuitive ADJ cana-942 149 7 statistic statistic NOUN cana-942 149 8 is be AUX cana-942 149 9 this this DET cana-942 149 10 one one NOUN cana-942 149 11 : : PUNCT cana-942 149 12 it it PRON cana-942 149 13 shows show VERB cana-942 149 14 how how SCONJ cana-942 149 15 many many ADJ cana-942 149 16 occurrences occurrence NOUN cana-942 149 17 out out ADP cana-942 149 18 of of ADP cana-942 149 19 all all DET cana-942 149 20 the the DET cana-942 149 21 examples example NOUN cana-942 149 22 in in ADP cana-942 149 23 the the DET cana-942 149 24 dataset dataset NOUN cana-942 149 25 have have AUX cana-942 149 26 been be AUX cana-942 149 27 properly properly ADV cana-942 149 28 classified classify VERB cana-942 149 29 , , PUNCT cana-942 149 30 relative relative ADJ cana-942 149 31 to to ADP cana-942 149 32 the the DET cana-942 149 33 total total ADJ cana-942 149 34 number number NOUN cana-942 149 35 of of ADP cana-942 149 36 examples example NOUN cana-942 149 37 . . PUNCT cana-942 150 1 a a DET cana-942 150 2 dataset dataset NOUN cana-942 150 3 's 's PART cana-942 150 4 accuracy accuracy NOUN cana-942 150 5 may may AUX cana-942 150 6 be be AUX cana-942 150 7 determined determine VERB cana-942 150 8 by by ADP cana-942 150 9 dividing divide VERB cana-942 150 10 its its PRON cana-942 150 11 total total ADJ cana-942 150 12 occurrences occurrence NOUN cana-942 150 13 by by ADP cana-942 150 14 its its PRON cana-942 150 15 true true ADJ cana-942 150 16 positive positive ADJ cana-942 150 17 and and CCONJ cana-942 150 18 true true ADJ cana-942 150 19 negative negative ADJ cana-942 150 20 counts count NOUN cana-942 150 21 ( ( PUNCT cana-942 150 22 which which PRON cana-942 150 23 include include VERB cana-942 150 24 all all DET cana-942 150 25 positive positive ADJ cana-942 150 26 and and CCONJ cana-942 150 27 negative negative ADJ cana-942 150 28 values value NOUN cana-942 150 29 as as ADV cana-942 150 30 well well ADV cana-942 150 31 as as ADP cana-942 150 32 any any DET cana-942 150 33 false false ADJ cana-942 150 34 positives positive NOUN cana-942 150 35 or or CCONJ cana-942 150 36 negatives negative NOUN cana-942 150 37 ) ) PUNCT cana-942 150 38 ( ( PUNCT cana-942 150 39 equation equation NOUN cana-942 150 40 4 4 NUM cana-942 150 41 ) ) PUNCT cana-942 150 42 . . PUNCT cana-942 151 1 communications communication NOUN cana-942 151 2 on on ADP cana-942 151 3 applied apply VERB cana-942 151 4 nonlinear nonlinear ADJ cana-942 151 5 analysis analysis NOUN cana-942 151 6 issn issn NOUN cana-942 151 7 : : PUNCT cana-942 151 8 1074 1074 NUM cana-942 151 9 - - PUNCT cana-942 151 10 133x 133x NUM cana-942 151 11 vol vol NOUN cana-942 151 12 31 31 NUM cana-942 151 13 no no NOUN cana-942 151 14 . . PUNCT cana-942 152 1 4s 4s NUM cana-942 152 2 ( ( PUNCT cana-942 152 3 2024 2024 NUM cana-942 152 4 ) ) PUNCT cana-942 152 5 457 457 NUM cana-942 152 6 https://internationalpubls.com https://internationalpubls.com X cana-942 152 7 accuracy accuracy NOUN cana-942 152 8 = = SYM cana-942 152 9 𝑇𝑃 𝑇𝑃 PROPN cana-942 152 10 + + CCONJ cana-942 152 11 𝑇𝑁 𝑇𝑁 PROPN cana-942 152 12 𝑇𝑃 𝑇𝑃 PROPN cana-942 152 13 + + CCONJ cana-942 152 14 𝐹𝑃 𝐹𝑃 PROPN cana-942 153 1 + + CCONJ cana-942 153 2 𝑇𝑁 𝑇𝑁 ADJ cana-942 153 3 + + CCONJ cana-942 153 4 𝐹𝑁 𝐹𝑁 PROPN cana-942 153 5 recall recall NOUN cana-942 153 6 : : PUNCT cana-942 153 7 this this DET cana-942 153 8 metric metric NOUN cana-942 153 9 has have VERB cana-942 153 10 several several ADJ cana-942 153 11 names name NOUN cana-942 153 12 , , PUNCT cana-942 153 13 such such ADJ cana-942 153 14 as as ADP cana-942 153 15 sensitivity sensitivity NOUN cana-942 153 16 or or CCONJ cana-942 153 17 the the DET cana-942 153 18 true true ADJ cana-942 153 19 positive positive ADJ cana-942 153 20 rate rate NOUN cana-942 153 21 . . PUNCT cana-942 154 1 by by ADP cana-942 154 2 calculating calculate VERB cana-942 154 3 the the DET cana-942 154 4 true true ADJ cana-942 154 5 positive positive ADJ cana-942 154 6 percentage percentage NOUN cana-942 154 7 , , PUNCT cana-942 154 8 it it PRON cana-942 154 9 finds find VERB cana-942 154 10 out out ADP cana-942 154 11 what what DET cana-942 154 12 fraction fraction NOUN cana-942 154 13 of of ADP cana-942 154 14 the the DET cana-942 154 15 dataset dataset NOUN cana-942 154 16 's 's PART cana-942 154 17 genuine genuine ADJ cana-942 154 18 positives positive NOUN cana-942 154 19 are be AUX cana-942 154 20 accurate accurate ADJ cana-942 154 21 positives positive NOUN cana-942 154 22 . . PUNCT cana-942 155 1 equation equation NOUN cana-942 155 2 5 5 NUM cana-942 155 3 states state NOUN cana-942 155 4 that that SCONJ cana-942 155 5 it it PRON cana-942 155 6 may may AUX cana-942 155 7 be be AUX cana-942 155 8 calculated calculate VERB cana-942 155 9 by by ADP cana-942 155 10 dividing divide VERB cana-942 155 11 tp tp NOUN cana-942 155 12 by by ADP cana-942 155 13 the the DET cana-942 155 14 sum sum NOUN cana-942 155 15 of of ADP cana-942 155 16 tp tp NOUN cana-942 155 17 and and CCONJ cana-942 156 1 fn fn PROPN cana-942 156 2 . . PROPN cana-942 156 3 recall recall PROPN cana-942 156 4 is be AUX cana-942 156 5 useful useful ADJ cana-942 156 6 when when SCONJ cana-942 156 7 we we PRON cana-942 156 8 want want VERB cana-942 156 9 to to PART cana-942 156 10 find find VERB cana-942 156 11 all all DET cana-942 156 12 positive positive ADJ cana-942 156 13 examples example NOUN cana-942 156 14 with with ADP cana-942 156 15 few few ADJ cana-942 156 16 false false ADJ cana-942 156 17 negatives negative NOUN cana-942 156 18 . . PUNCT cana-942 157 1 recall recall NOUN cana-942 157 2 = = SYM cana-942 157 3 𝑇𝑃 𝑇𝑃 PROPN cana-942 157 4 𝑇𝑃 𝑇𝑃 PROPN cana-942 157 5 + + CCONJ cana-942 157 6 𝐹𝑁 𝐹𝑁 PROPN cana-942 157 7 f1 f1 NOUN cana-942 157 8 score score NOUN cana-942 157 9 : : PUNCT cana-942 157 10 when when SCONJ cana-942 157 11 it it PRON cana-942 157 12 comes come VERB cana-942 157 13 to to ADP cana-942 157 14 accessibility accessibility NOUN cana-942 157 15 and and CCONJ cana-942 157 16 precision precision NOUN cana-942 157 17 , , PUNCT cana-942 157 18 this this PRON cana-942 157 19 is be AUX cana-942 157 20 the the DET cana-942 157 21 sweet sweet ADJ cana-942 157 22 spot spot NOUN cana-942 157 23 . . PUNCT cana-942 158 1 it it PRON cana-942 158 2 is be AUX cana-942 158 3 a a DET cana-942 158 4 helpful helpful ADJ cana-942 158 5 statistic statistic NOUN cana-942 158 6 when when SCONJ cana-942 158 7 both both DET cana-942 158 8 recall recall VERB cana-942 158 9 and and CCONJ cana-942 158 10 accuracy accuracy NOUN cana-942 158 11 should should AUX cana-942 158 12 be be AUX cana-942 158 13 considered consider VERB cana-942 158 14 since since SCONJ cana-942 158 15 it it PRON cana-942 158 16 finds find VERB cana-942 158 17 a a DET cana-942 158 18 middle middle ADJ cana-942 158 19 ground ground NOUN cana-942 158 20 between between ADP cana-942 158 21 the the DET cana-942 158 22 two two NUM cana-942 158 23 . . PUNCT cana-942 159 1 it it PRON cana-942 159 2 is be AUX cana-942 159 3 calculated calculate VERB cana-942 159 4 as as SCONJ cana-942 159 5 shown show VERB cana-942 159 6 in in ADP cana-942 159 7 equation equation NOUN cana-942 159 8 6 6 NUM cana-942 159 9 . . PUNCT cana-942 160 1 f1 f1 NOUN cana-942 160 2 scores score NOUN cana-942 160 3 may may AUX cana-942 160 4 be be AUX cana-942 160 5 anything anything PRON cana-942 160 6 from from ADP cana-942 160 7 0 0 NUM cana-942 160 8 ( ( PUNCT cana-942 160 9 very very ADV cana-942 160 10 bad bad ADJ cana-942 160 11 ) ) PUNCT cana-942 160 12 to to ADP cana-942 160 13 1 1 NUM cana-942 160 14 ( ( PUNCT cana-942 160 15 very very ADV cana-942 160 16 good good ADJ cana-942 160 17 ) ) PUNCT cana-942 160 18 . . PUNCT cana-942 161 1 f1 f1 NOUN cana-942 161 2 score score NOUN cana-942 161 3 = = SYM cana-942 161 4 2 2 NUM cana-942 161 5 π‘₯ π‘₯ PRON cana-942 161 6 π‘ƒπ‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› π‘ƒπ‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› PROPN cana-942 161 7 π‘₯ π‘₯ PROPN cana-942 161 8 π‘Ÿπ‘’π‘π‘Žπ‘™π‘™ π‘Ÿπ‘’π‘π‘Žπ‘™π‘™ ADJ cana-942 161 9 π‘ƒπ‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› π‘ƒπ‘Ÿπ‘’π‘π‘–π‘ π‘–π‘œπ‘› PROPN cana-942 161 10 π‘₯ π‘₯ ADP cana-942 161 11 π‘…π‘’π‘π‘Žπ‘™π‘™ π‘…π‘’π‘π‘Žπ‘™π‘™ PROPN cana-942 161 12 results result VERB cana-942 161 13 for for ADP cana-942 161 14 algorithms algorithm NOUN cana-942 161 15 : : PUNCT cana-942 161 16 we we PRON cana-942 161 17 used use VERB cana-942 161 18 all all DET cana-942 161 19 the the DET cana-942 161 20 previously previously ADV cana-942 161 21 mentioned mention VERB cana-942 161 22 strategies strategy NOUN cana-942 161 23 to to PART cana-942 161 24 build build VERB cana-942 161 25 the the DET cana-942 161 26 dataset dataset NOUN cana-942 161 27 - - PUNCT cana-942 161 28 based base VERB cana-942 161 29 regression regression NOUN cana-942 161 30 and and CCONJ cana-942 161 31 classification classification NOUN cana-942 161 32 model model NOUN cana-942 161 33 . . PUNCT cana-942 162 1 to to PART cana-942 162 2 evaluate evaluate VERB cana-942 162 3 the the DET cana-942 162 4 model model NOUN cana-942 162 5 , , PUNCT cana-942 162 6 the the DET cana-942 162 7 hyperparameter hyperparameter NOUN cana-942 162 8 tweaking tweaking NOUN cana-942 162 9 method method NOUN cana-942 162 10 was be AUX cana-942 162 11 used use VERB cana-942 162 12 . . PUNCT cana-942 163 1 table table NOUN cana-942 163 2 2 2 NUM cana-942 163 3 comparison comparison NOUN cana-942 163 4 of of ADP cana-942 163 5 different different ADJ cana-942 163 6 classifiers classifier NOUN cana-942 163 7 model model NOUN cana-942 163 8 accuracy accuracy NOUN cana-942 163 9 score score NOUN cana-942 163 10 1 1 NUM cana-942 163 11 svm svm VERB cana-942 163 12 0.688540 0.688540 NUM cana-942 163 13 2 2 NUM cana-942 163 14 xgboost xgboost ADP cana-942 163 15 0.670980 0.670980 NUM cana-942 163 16 3 3 NUM cana-942 163 17 kneighbours kneighbour NOUN cana-942 163 18 0.653420 0.653420 NUM cana-942 163 19 4 4 NUM cana-942 163 20 decision decision NOUN cana-942 163 21 tree tree NOUN cana-942 163 22 0.645102 0.645102 NUM cana-942 163 23 5 5 NUM cana-942 163 24 adaboost adaboost ADV cana-942 163 25 0.634011 0.634011 NUM cana-942 163 26 6 6 NUM cana-942 163 27 logistic logistic ADJ cana-942 163 28 regression regression NOUN cana-942 163 29 0.628466 0.628466 NUM cana-942 163 30 7 7 NUM cana-942 163 31 random random ADJ cana-942 163 32 forest forest NOUN cana-942 163 33 0.628466 0.628466 NUM cana-942 163 34 fig fig NOUN cana-942 163 35 8 8 NUM cana-942 163 36 accuracy accuracy NOUN cana-942 163 37 score score NOUN cana-942 163 38 15 15 NUM cana-942 163 39 % % NOUN cana-942 163 40 15 15 NUM cana-942 163 41 % % NOUN cana-942 163 42 14 14 NUM cana-942 163 43 % % NOUN cana-942 163 44 14 14 NUM cana-942 163 45 % % NOUN cana-942 163 46 14 14 NUM cana-942 163 47 % % NOUN cana-942 163 48 14 14 NUM cana-942 163 49 % % NOUN cana-942 163 50 14 14 NUM cana-942 163 51 % % NOUN cana-942 163 52 accuracy accuracy NOUN cana-942 163 53 score score NOUN cana-942 163 54 1 1 NUM cana-942 163 55 svm svm NOUN cana-942 163 56 2 2 NUM cana-942 163 57 xgboost xgboost NOUN cana-942 163 58 3 3 NUM cana-942 163 59 kneighbours kneighbour NOUN cana-942 163 60 4 4 NUM cana-942 163 61 decision decision NOUN cana-942 163 62 tree tree NOUN cana-942 163 63 5 5 NUM cana-942 163 64 adaboost adaboost ADV cana-942 163 65 6 6 NUM cana-942 163 66 logistic logistic ADJ cana-942 163 67 regression regression NOUN cana-942 163 68 7 7 NUM cana-942 163 69 random random ADJ cana-942 163 70 forest forest NOUN cana-942 163 71 communications communication NOUN cana-942 163 72 on on ADP cana-942 163 73 applied apply VERB cana-942 163 74 nonlinear nonlinear ADJ cana-942 163 75 analysis analysis NOUN cana-942 163 76 issn issn NOUN cana-942 163 77 : : PUNCT cana-942 163 78 1074 1074 NUM cana-942 163 79 - - PUNCT cana-942 163 80 133x 133x NUM cana-942 163 81 vol vol NOUN cana-942 163 82 31 31 NUM cana-942 163 83 no no NOUN cana-942 163 84 . . PUNCT cana-942 164 1 4s 4s NUM cana-942 164 2 ( ( PUNCT cana-942 164 3 2024 2024 NUM cana-942 164 4 ) ) PUNCT cana-942 164 5 458 458 NUM cana-942 164 6 https://internationalpubls.com https://internationalpubls.com X cana-942 164 7 hyperparameter hyperparameter NOUN cana-942 164 8 tunning tun VERB cana-942 164 9 when when SCONJ cana-942 164 10 it it PRON cana-942 164 11 comes come VERB cana-942 164 12 to to ADP cana-942 164 13 machine machine NOUN cana-942 164 14 learning learning NOUN cana-942 164 15 models model NOUN cana-942 164 16 , , PUNCT cana-942 164 17 " " PUNCT cana-942 164 18 hyperparameter hyperparameter NOUN cana-942 164 19 tuning tuning NOUN cana-942 164 20 " " PUNCT cana-942 164 21 refers refer VERB cana-942 164 22 to to ADP cana-942 164 23 finding find VERB cana-942 164 24 the the DET cana-942 164 25 sweet sweet ADJ cana-942 164 26 spot spot NOUN cana-942 164 27 for for ADP cana-942 164 28 each each DET cana-942 164 29 model model NOUN cana-942 164 30 's 's PART cana-942 164 31 hyperparameters hyperparameter NOUN cana-942 164 32 . . PUNCT cana-942 165 1 here here ADV cana-942 165 2 , , PUNCT cana-942 165 3 we we PRON cana-942 165 4 talk talk VERB cana-942 165 5 about about ADP cana-942 165 6 " " PUNCT cana-942 165 7 hyperparameter hyperparameter NOUN cana-942 165 8 tuning tuning NOUN cana-942 165 9 . . PUNCT cana-942 165 10 " " PUNCT cana-942 166 1 the the DET cana-942 166 2 learning learning NOUN cana-942 166 3 rate rate NOUN cana-942 166 4 is be AUX cana-942 166 5 a a DET cana-942 166 6 hyperparameter hyperparameter NOUN cana-942 166 7 . . PUNCT cana-942 167 1 the the DET cana-942 167 2 batch batch NOUN cana-942 167 3 size size NOUN cana-942 167 4 , , PUNCT cana-942 167 5 hidden hide VERB cana-942 167 6 layer layer NOUN cana-942 167 7 count count NOUN cana-942 167 8 , , PUNCT cana-942 167 9 and and CCONJ cana-942 167 10 neuron neuron PROPN cana-942 167 11 count count NOUN cana-942 167 12 per per ADP cana-942 167 13 hidden hide VERB cana-942 167 14 layer layer NOUN cana-942 167 15 are be AUX cana-942 167 16 some some PRON cana-942 167 17 more more ADJ cana-942 167 18 examples example NOUN cana-942 167 19 . . PUNCT cana-942 168 1 because because SCONJ cana-942 168 2 these these DET cana-942 168 3 model model NOUN cana-942 168 4 parameters parameter NOUN cana-942 168 5 can can AUX cana-942 168 6 not not PART cana-942 168 7 be be AUX cana-942 168 8 learned learn VERB cana-942 168 9 during during ADP cana-942 168 10 training training NOUN cana-942 168 11 , , PUNCT cana-942 168 12 they they PRON cana-942 168 13 must must AUX cana-942 168 14 be be AUX cana-942 168 15 supplied supply VERB cana-942 168 16 before before SCONJ cana-942 168 17 training training NOUN cana-942 168 18 begins begin VERB cana-942 168 19 . . PUNCT cana-942 169 1 hyperparameter hyperparameter NOUN cana-942 169 2 tweaking tweaking NOUN cana-942 169 3 may may AUX cana-942 169 4 be be AUX cana-942 169 5 accomplished accomplish VERB cana-942 169 6 in in ADP cana-942 169 7 several several ADJ cana-942 169 8 ways way NOUN cana-942 169 9 . . PUNCT cana-942 170 1 manual manual ADJ cana-942 170 2 tuning tuning NOUN cana-942 170 3 , , PUNCT cana-942 170 4 random random ADJ cana-942 170 5 search search NOUN cana-942 170 6 , , PUNCT cana-942 170 7 grid grid NOUN cana-942 170 8 search search NOUN cana-942 170 9 , , PUNCT cana-942 170 10 and and CCONJ cana-942 170 11 bayesian bayesian NOUN cana-942 170 12 optimization optimization NOUN cana-942 170 13 are be AUX cana-942 170 14 a a DET cana-942 170 15 few few ADJ cana-942 170 16 examples example NOUN cana-942 170 17 of of ADP cana-942 170 18 these these DET cana-942 170 19 strategies strategy NOUN cana-942 170 20 . . PUNCT cana-942 171 1 gridsearchcv gridsearchcv NOUN cana-942 171 2 : : PUNCT cana-942 171 3 the the DET cana-942 171 4 user user NOUN cana-942 171 5 is be AUX cana-942 171 6 asked ask VERB cana-942 171 7 to to PART cana-942 171 8 submit submit VERB cana-942 171 9 a a DET cana-942 171 10 grid grid NOUN cana-942 171 11 of of ADP cana-942 171 12 possible possible ADJ cana-942 171 13 hyperparameters hyperparameter NOUN cana-942 171 14 before before ADP cana-942 171 15 gridsearchcv gridsearchcv NOUN cana-942 171 16 searches search NOUN cana-942 171 17 exhaustively exhaustively ADV cana-942 171 18 over over ADP cana-942 171 19 all all DET cana-942 171 20 possible possible ADJ cana-942 171 21 combinations combination NOUN cana-942 171 22 of of ADP cana-942 171 23 those those DET cana-942 171 24 hyperparameters hyperparameter NOUN cana-942 171 25 . . PUNCT cana-942 172 1 for for ADP cana-942 172 2 every every DET cana-942 172 3 conceivable conceivable ADJ cana-942 172 4 combination combination NOUN cana-942 172 5 of of ADP cana-942 172 6 hyperparameters hyperparameter NOUN cana-942 172 7 , , PUNCT cana-942 172 8 gridsearchcv gridsearchcv NOUN cana-942 172 9 runs run VERB cana-942 172 10 a a DET cana-942 172 11 cross cross ADJ cana-942 172 12 - - ADJ cana-942 172 13 validation validation ADJ cana-942 172 14 test test NOUN cana-942 172 15 on on ADP cana-942 172 16 the the DET cana-942 172 17 training training NOUN cana-942 172 18 data datum NOUN cana-942 172 19 to to PART cana-942 172 20 assess assess VERB cana-942 172 21 the the DET cana-942 172 22 model model NOUN cana-942 172 23 's 's PART cana-942 172 24 performance performance NOUN cana-942 172 25 . . PUNCT cana-942 173 1 the the DET cana-942 173 2 ideal ideal ADJ cana-942 173 3 hyperparameters hyperparameter NOUN cana-942 173 4 are be AUX cana-942 173 5 those those PRON cana-942 173 6 that that PRON cana-942 173 7 provide provide VERB cana-942 173 8 the the DET cana-942 173 9 greatest great ADJ cana-942 173 10 results result NOUN cana-942 173 11 in in ADP cana-942 173 12 terms term NOUN cana-942 173 13 of of ADP cana-942 173 14 performance performance NOUN cana-942 173 15 . . PUNCT cana-942 174 1 randomizedsearchcv randomizedsearchcv PROPN cana-942 174 2 : : PUNCT cana-942 174 3 creates create VERB cana-942 174 4 a a DET cana-942 174 5 set set NOUN cana-942 174 6 of of ADP cana-942 174 7 hyperparameter hyperparameter NOUN cana-942 174 8 combinations combination NOUN cana-942 174 9 by by ADP cana-942 174 10 randomly randomly ADV cana-942 174 11 selecting select VERB cana-942 174 12 from from ADP cana-942 174 13 each each PRON cana-942 174 14 of of ADP cana-942 174 15 the the DET cana-942 174 16 distributions distribution NOUN cana-942 174 17 that that PRON cana-942 174 18 are be AUX cana-942 174 19 described describe VERB cana-942 174 20 for for ADP cana-942 174 21 each each DET cana-942 174 22 hyperparameter hyperparameter NOUN cana-942 174 23 . . PUNCT cana-942 175 1 in in ADP cana-942 175 2 order order NOUN cana-942 175 3 to to PART cana-942 175 4 evaluate evaluate VERB cana-942 175 5 the the DET cana-942 175 6 efficacy efficacy NOUN cana-942 175 7 of of ADP cana-942 175 8 a a DET cana-942 175 9 model model NOUN cana-942 175 10 , , PUNCT cana-942 175 11 randomizedsearchcv randomizedsearchcv NOUN cana-942 175 12 employs employ VERB cana-942 175 13 a a DET cana-942 175 14 technique technique NOUN cana-942 175 15 known know VERB cana-942 175 16 as as ADP cana-942 175 17 cross cross NOUN cana-942 175 18 - - NOUN cana-942 175 19 validation validation NOUN cana-942 175 20 on on ADP cana-942 175 21 the the DET cana-942 175 22 training training NOUN cana-942 175 23 data datum NOUN cana-942 175 24 . . PUNCT cana-942 176 1 this this DET cana-942 176 2 process process NOUN cana-942 176 3 is be AUX cana-942 176 4 repeated repeat VERB cana-942 176 5 for for ADP cana-942 176 6 every every DET cana-942 176 7 conceivable conceivable ADJ cana-942 176 8 combination combination NOUN cana-942 176 9 . . PUNCT cana-942 177 1 the the DET cana-942 177 2 hyperparameters hyperparameter NOUN cana-942 177 3 that that PRON cana-942 177 4 provide provide VERB cana-942 177 5 the the DET cana-942 177 6 greatest great ADJ cana-942 177 7 results result NOUN cana-942 177 8 are be AUX cana-942 177 9 chosen choose VERB cana-942 177 10 as as ADP cana-942 177 11 the the DET cana-942 177 12 best good ADJ cana-942 177 13 . . PUNCT cana-942 178 1 bayesian bayesian NOUN cana-942 178 2 optimization optimization NOUN cana-942 178 3 : : PUNCT cana-942 178 4 using use VERB cana-942 178 5 probabilistic probabilistic ADJ cana-942 178 6 models model NOUN cana-942 178 7 , , PUNCT cana-942 178 8 bayesian bayesian NOUN cana-942 178 9 optimization optimization NOUN cana-942 178 10 guides guide VERB cana-942 178 11 the the DET cana-942 178 12 search search NOUN cana-942 178 13 for for ADP cana-942 178 14 the the DET cana-942 178 15 optimum optimum ADJ cana-942 178 16 hyperparameter hyperparameter NOUN cana-942 178 17 combination combination NOUN cana-942 178 18 , , PUNCT cana-942 178 19 increasing increase VERB cana-942 178 20 the the DET cana-942 178 21 likelihood likelihood NOUN cana-942 178 22 of of ADP cana-942 178 23 achieving achieve VERB cana-942 178 24 optimal optimal ADJ cana-942 178 25 outcomes outcome NOUN cana-942 178 26 . . PUNCT cana-942 179 1 because because SCONJ cana-942 179 2 it it PRON cana-942 179 3 is be AUX cana-942 179 4 able able ADJ cana-942 179 5 to to PART cana-942 179 6 zero zero VERB cana-942 179 7 in in ADP cana-942 179 8 on on ADP cana-942 179 9 the the DET cana-942 179 10 most most ADV cana-942 179 11 promising promising ADJ cana-942 179 12 regions region NOUN cana-942 179 13 of of ADP cana-942 179 14 the the DET cana-942 179 15 search search NOUN cana-942 179 16 space space NOUN cana-942 179 17 and and CCONJ cana-942 179 18 find find VERB cana-942 179 19 intriguing intriguing ADJ cana-942 179 20 hyperparameters hyperparameter NOUN cana-942 179 21 early early ADV cana-942 179 22 on on ADV cana-942 179 23 , , PUNCT cana-942 179 24 this this DET cana-942 179 25 method method NOUN cana-942 179 26 outperforms outperform VERB cana-942 179 27 grid grid NOUN cana-942 179 28 search search NOUN cana-942 179 29 and and CCONJ cana-942 179 30 random random ADJ cana-942 179 31 search search NOUN cana-942 179 32 . . PUNCT cana-942 180 1 both both PRON cana-942 180 2 grid grid NOUN cana-942 180 3 search search NOUN cana-942 180 4 and and CCONJ cana-942 180 5 random random ADJ cana-942 180 6 search search NOUN cana-942 180 7 aim aim NOUN cana-942 180 8 to to PART cana-942 180 9 cover cover VERB cana-942 180 10 the the DET cana-942 180 11 whole whole ADJ cana-942 180 12 search search NOUN cana-942 180 13 space space NOUN cana-942 180 14 in in ADP cana-942 180 15 one one NUM cana-942 180 16 go go NOUN cana-942 180 17 . . PUNCT cana-942 181 1 results result NOUN cana-942 181 2 of of ADP cana-942 181 3 hyperparameter hyperparameter NOUN cana-942 181 4 tunning tun VERB cana-942 181 5 : : PUNCT cana-942 181 6 classifiers classifier NOUN cana-942 181 7 like like VERB cana-942 181 8 rf rf PRON cana-942 181 9 see see VERB cana-942 181 10 an an DET cana-942 181 11 improvement improvement NOUN cana-942 181 12 in in ADP cana-942 181 13 top top ADJ cana-942 181 14 - - PUNCT cana-942 181 15 level level NOUN cana-942 181 16 accuracy accuracy NOUN cana-942 181 17 and and CCONJ cana-942 181 18 precision precision NOUN cana-942 181 19 after after SCONJ cana-942 181 20 hyperparameter hyperparameter NOUN cana-942 181 21 tinkering tinker VERB cana-942 181 22 , , PUNCT cana-942 181 23 but but CCONJ cana-942 181 24 a a DET cana-942 181 25 decline decline NOUN cana-942 181 26 in in ADP cana-942 181 27 accuracy accuracy NOUN cana-942 181 28 in in ADP cana-942 181 29 other other ADJ cana-942 181 30 cases case NOUN cana-942 181 31 , , PUNCT cana-942 181 32 as as SCONJ cana-942 181 33 seen see VERB cana-942 181 34 in in ADP cana-942 181 35 table table NOUN cana-942 181 36 ( ( PUNCT cana-942 181 37 4 4 NUM cana-942 181 38 ) ) PUNCT cana-942 181 39 . . PUNCT cana-942 182 1 in in ADP cana-942 182 2 contrast contrast NOUN cana-942 182 3 , , PUNCT cana-942 182 4 there there PRON cana-942 182 5 are be VERB cana-942 182 6 other other ADJ cana-942 182 7 contexts context NOUN cana-942 182 8 when when SCONJ cana-942 182 9 precision precision NOUN cana-942 182 10 degrades degrade VERB cana-942 182 11 . . PUNCT cana-942 183 1 table table NOUN cana-942 183 2 3 3 NUM cana-942 183 3 results result NOUN cana-942 183 4 of of ADP cana-942 183 5 hyperparameter hyperparameter NOUN cana-942 183 6 tuning tune VERB cana-942 183 7 model model NOUN cana-942 183 8 accuracy accuracy NOUN cana-942 183 9 before before ADP cana-942 183 10 hyperparameter hyperparameter NOUN cana-942 183 11 tunning tunning NOUN cana-942 183 12 accuracy accuracy NOUN cana-942 183 13 after after ADP cana-942 183 14 hyperparameter hyperparameter NOUN cana-942 183 15 tunning tun VERB cana-942 183 16 best good ADJ cana-942 183 17 test test NOUN cana-942 183 18 score score NOUN cana-942 183 19 score score NOUN cana-942 183 20 svc svc PROPN cana-942 183 21 0.688 0.688 NUM cana-942 183 22 0.605 0.605 NUM cana-942 183 23 0.628 0.628 NUM cana-942 183 24 xgboost xgboost ADP cana-942 183 25 0.670 0.670 NUM cana-942 183 26 0.649 0.649 NUM cana-942 183 27 0.667 0.667 NUM cana-942 183 28 knn knn VERB cana-942 183 29 0.653 0.653 NUM cana-942 183 30 0.637 0.637 NUM cana-942 183 31 0.637 0.637 NUM cana-942 183 32 dt dt NOUN cana-942 183 33 0.645 0.645 NUM cana-942 183 34 0.632 0.632 NUM cana-942 183 35 0.63 0.63 NUM cana-942 183 36 adaboost adaboost VERB cana-942 183 37 0.634 0.634 NUM cana-942 183 38 0.637 0.637 NUM cana-942 183 39 0.64 0.64 NUM cana-942 183 40 logestic logestic ADJ cana-942 183 41 regression regression NOUN cana-942 183 42 0.628 0.628 NUM cana-942 183 43 0.605 0.605 NUM cana-942 183 44 0.6 0.6 NUM cana-942 183 45 communications communication NOUN cana-942 183 46 on on ADP cana-942 183 47 applied apply VERB cana-942 183 48 nonlinear nonlinear ADJ cana-942 183 49 analysis analysis NOUN cana-942 183 50 issn issn NOUN cana-942 183 51 : : PUNCT cana-942 183 52 1074 1074 NUM cana-942 183 53 - - PUNCT cana-942 183 54 133x 133x NUM cana-942 183 55 vol vol NOUN cana-942 183 56 31 31 NUM cana-942 183 57 no no NOUN cana-942 183 58 . . PUNCT cana-942 184 1 4s 4s NUM cana-942 184 2 ( ( PUNCT cana-942 184 3 2024 2024 NUM cana-942 184 4 ) ) PUNCT cana-942 184 5 459 459 NUM cana-942 184 6 https://internationalpubls.com https://internationalpubls.com X cana-942 184 7 fig fig NOUN cana-942 184 8 9 9 NUM cana-942 184 9 hyperparameter hyperparameter NOUN cana-942 184 10 tuning tune VERB cana-942 184 11 6 6 NUM cana-942 184 12 . . PUNCT cana-942 184 13 results result NOUN cana-942 184 14 using use VERB cana-942 184 15 a a DET cana-942 184 16 dataset dataset NOUN cana-942 184 17 including include VERB cana-942 184 18 information information NOUN cana-942 184 19 regarding regard VERB cana-942 184 20 water water NOUN cana-942 184 21 quality quality NOUN cana-942 184 22 , , PUNCT cana-942 184 23 this this DET cana-942 184 24 research research NOUN cana-942 184 25 tested test VERB cana-942 184 26 , , PUNCT cana-942 184 27 compared compare VERB cana-942 184 28 , , PUNCT cana-942 184 29 and and CCONJ cana-942 184 30 assessed assess VERB cana-942 184 31 the the DET cana-942 184 32 prediction prediction NOUN cana-942 184 33 capacities capacity NOUN cana-942 184 34 of of ADP cana-942 184 35 five five NUM cana-942 184 36 unique unique ADJ cana-942 184 37 machine machine NOUN cana-942 184 38 learning learn VERB cana-942 184 39 algorithms algorithm NOUN cana-942 184 40 . . PUNCT cana-942 185 1 this this DET cana-942 185 2 goal goal NOUN cana-942 185 3 was be AUX cana-942 185 4 accomplished accomplish VERB cana-942 185 5 by by ADP cana-942 185 6 collecting collect VERB cana-942 185 7 values value NOUN cana-942 185 8 from from ADP cana-942 185 9 popular popular ADJ cana-942 185 10 datasets dataset NOUN cana-942 185 11 , , PUNCT cana-942 185 12 including include VERB cana-942 185 13 those those PRON cana-942 185 14 for for ADP cana-942 185 15 ph ph ADJ cana-942 185 16 , , PUNCT cana-942 185 17 hardness hardness NOUN cana-942 185 18 , , PUNCT cana-942 185 19 solids solid NOUN cana-942 185 20 , , PUNCT cana-942 185 21 electrical electrical ADJ cana-942 185 22 conductivity conductivity NOUN cana-942 185 23 ( ( PUNCT cana-942 185 24 ec ec PROPN cana-942 185 25 ) ) PUNCT cana-942 185 26 , , PUNCT cana-942 185 27 and and CCONJ cana-942 185 28 turbidity turbidity NOUN cana-942 185 29 . . PUNCT cana-942 186 1 table table NOUN cana-942 186 2 3 3 NUM cana-942 186 3 shows show VERB cana-942 186 4 that that SCONJ cana-942 186 5 the the DET cana-942 186 6 results result NOUN cana-942 186 7 showed show VERB cana-942 186 8 that that SCONJ cana-942 186 9 the the DET cana-942 186 10 employed employ VERB cana-942 186 11 models model NOUN cana-942 186 12 performed perform VERB cana-942 186 13 adequately adequately ADV cana-942 186 14 in in ADP cana-942 186 15 forecasting forecast VERB cana-942 186 16 water water NOUN cana-942 186 17 quality quality NOUN cana-942 186 18 data datum NOUN cana-942 186 19 . . PUNCT cana-942 187 1 on on ADP cana-942 187 2 the the DET cana-942 187 3 other other ADJ cana-942 187 4 side side NOUN cana-942 187 5 , , PUNCT cana-942 187 6 rf rf PRON cana-942 187 7 and and CCONJ cana-942 187 8 xgb xgb NUM cana-942 187 9 excel excel VERB cana-942 187 10 in in ADP cana-942 187 11 terms term NOUN cana-942 187 12 of of ADP cana-942 187 13 performance performance NOUN cana-942 187 14 . . PUNCT cana-942 188 1 fig fig NOUN cana-942 188 2 10 10 NUM cana-942 188 3 actual actual ADJ cana-942 188 4 water water NOUN cana-942 188 5 quality quality NOUN cana-942 188 6 0.59 0.59 NUM cana-942 188 7 0.6 0.6 NUM cana-942 188 8 0.61 0.61 NUM cana-942 188 9 0.62 0.62 NUM cana-942 188 10 0.63 0.63 NUM cana-942 188 11 0.64 0.64 NUM cana-942 189 1 0.65 0.65 NUM cana-942 189 2 0.66 0.66 NUM cana-942 189 3 0.67 0.67 NUM cana-942 189 4 0.68 0.68 NUM cana-942 189 5 0.69 0.69 NUM cana-942 189 6 0.7 0.7 NUM cana-942 189 7 svc svc PROPN cana-942 189 8 xgboost xgboost PROPN cana-942 190 1 knn knn PROPN cana-942 190 2 dt dt PROPN cana-942 191 1 adaboost adaboost PROPN cana-942 191 2 logestic logestic ADJ cana-942 191 3 regression regression NOUN cana-942 191 4 hyperparameter hyperparameter NOUN cana-942 191 5 tuning tune VERB cana-942 191 6 accurac accurac NOUN cana-942 191 7 y y PROPN cana-942 191 8 before before SCONJ cana-942 191 9 hyperpa hyperpa ADV cana-942 191 10 rameter rameter VERB cana-942 191 11 tunning tunning NOUN cana-942 191 12 accuracy accuracy NOUN cana-942 191 13 after after ADP cana-942 191 14 hyperparameter hyperparameter NOUN cana-942 191 15 tunning tun VERB cana-942 191 16 best good ADJ cana-942 191 17 test test NOUN cana-942 191 18 score score NOUN cana-942 191 19 score score NOUN cana-942 191 20 communications communication NOUN cana-942 191 21 on on ADP cana-942 191 22 applied apply VERB cana-942 191 23 nonlinear nonlinear ADJ cana-942 191 24 analysis analysis NOUN cana-942 191 25 issn issn NOUN cana-942 191 26 : : PUNCT cana-942 191 27 1074 1074 NUM cana-942 191 28 - - PUNCT cana-942 191 29 133x 133x NUM cana-942 191 30 vol vol NOUN cana-942 191 31 31 31 NUM cana-942 191 32 no no NOUN cana-942 191 33 . . PUNCT cana-942 192 1 4s 4s NUM cana-942 192 2 ( ( PUNCT cana-942 192 3 2024 2024 NUM cana-942 192 4 ) ) PUNCT cana-942 192 5 460 460 NUM cana-942 193 1 https://internationalpubls.com https://internationalpubls.com X cana-942 193 2 table table NOUN cana-942 193 3 4 4 NUM cana-942 193 4 classification classification NOUN cana-942 193 5 report report NOUN cana-942 193 6 for for ADP cana-942 193 7 different different ADJ cana-942 193 8 ml ml NOUN cana-942 193 9 algorithm algorithm NOUN cana-942 193 10 model model NOUN cana-942 193 11 name name NOUN cana-942 193 12 class class NOUN cana-942 193 13 label label NOUN cana-942 193 14 precision precision NOUN cana-942 193 15 classification classification NOUN cana-942 193 16 report report NOUN cana-942 193 17 recall recall NOUN cana-942 193 18 f1score f1score NOUN cana-942 193 19 accuracy accuracy NOUN cana-942 193 20 svm svm VERB cana-942 193 21 not not PART cana-942 193 22 potable potable ADJ cana-942 193 23 potable potable ADJ cana-942 193 24 0.69 0.69 NUM cana-942 193 25 0.55 0.55 NUM cana-942 193 26 0.82 0.82 NUM cana-942 193 27 0.75 0.75 NUM cana-942 193 28 0.37 0.37 NUM cana-942 193 29 0.44 0.44 NUM cana-942 193 30 0.60 0.60 NUM cana-942 193 31 xgboost xgboost ADV cana-942 193 32 not not PART cana-942 193 33 potable potable ADJ cana-942 193 34 potable potable ADJ cana-942 193 35 0.68 0.68 NUM cana-942 193 36 0.61 0.61 NUM cana-942 193 37 0.89 0.89 NUM cana-942 193 38 0.77 0.77 NUM cana-942 193 39 0.31 0.31 NUM cana-942 193 40 0.41 0.41 NUM cana-942 193 41 0.64 0.64 NUM cana-942 193 42 kneighbours kneighbour NOUN cana-942 193 43 not not PART cana-942 193 44 0.69 0.69 NUM cana-942 193 45 0.55 0.55 NUM cana-942 193 46 0.82 0.82 NUM cana-942 193 47 0.75 0.75 NUM cana-942 193 48 0.37 0.37 NUM cana-942 193 49 0.44 0.44 NUM cana-942 193 50 0.63 0.63 NUM cana-942 193 51 decision decision NOUN cana-942 193 52 tree tree NOUN cana-942 193 53 not not PART cana-942 193 54 potable potable ADJ cana-942 193 55 potable potable ADJ cana-942 193 56 0.66 0.66 NUM cana-942 193 57 0.56 0.56 NUM cana-942 193 58 0.90 0.90 NUM cana-942 193 59 0.76 0.76 NUM cana-942 193 60 0.22 0.22 NUM cana-942 193 61 0.32 0.32 NUM cana-942 193 62 0.63 0.63 NUM cana-942 193 63 adaboost adaboost ADV cana-942 193 64 not not PART cana-942 193 65 potable potable ADJ cana-942 193 66 potable potable ADJ cana-942 193 67 0.63 0.63 NUM cana-942 193 68 0.62 0.62 NUM cana-942 193 69 0.99 0.99 NUM cana-942 193 70 0.77 0.77 NUM cana-942 193 71 0.04 0.04 NUM cana-942 193 72 0.07 0.07 NUM cana-942 193 73 0.62 0.62 NUM cana-942 193 74 logistic logistic ADJ cana-942 193 75 regression regression NOUN cana-942 193 76 not not PART cana-942 193 77 potable potable ADJ cana-942 193 78 potable potable ADJ cana-942 193 79 0.63 0.63 NUM cana-942 193 80 0.00 0.00 NUM cana-942 193 81 1.00 1.00 NUM cana-942 193 82 0.77 0.77 NUM cana-942 193 83 0.00 0.00 NUM cana-942 193 84 0.00 0.00 NUM cana-942 193 85 0.60 0.60 NUM cana-942 193 86 random random ADJ cana-942 193 87 forest forest NOUN cana-942 193 88 not not PART cana-942 193 89 potable potable ADJ cana-942 193 90 potable potable ADJ cana-942 193 91 0.63 0.63 NUM cana-942 193 92 0.00 0.00 NUM cana-942 193 93 1.00 1.00 NUM cana-942 193 94 0.77 0.77 NUM cana-942 193 95 0.00 0.00 NUM cana-942 193 96 0.00 0.00 NUM cana-942 193 97 0.67 0.67 NUM cana-942 193 98 fig fig NOUN cana-942 193 99 11 11 NUM cana-942 193 100 different different ADJ cana-942 193 101 ml ml NOUN cana-942 193 102 algorithm algorithm NOUN cana-942 193 103 7 7 NUM cana-942 193 104 . . PUNCT cana-942 193 105 discussion discussion NOUN cana-942 193 106 this this DET cana-942 193 107 research research NOUN cana-942 193 108 delves delve VERB cana-942 193 109 deeply deeply ADV cana-942 193 110 into into ADP cana-942 193 111 the the DET cana-942 193 112 difficulties difficulty NOUN cana-942 193 113 and and CCONJ cana-942 193 114 successes success NOUN cana-942 193 115 of of ADP cana-942 193 116 coastal coastal ADJ cana-942 193 117 water water NOUN cana-942 193 118 quality quality NOUN cana-942 193 119 prediction prediction NOUN cana-942 193 120 , , PUNCT cana-942 193 121 focusing focus VERB cana-942 193 122 on on ADP cana-942 193 123 how how SCONJ cana-942 193 124 to to PART cana-942 193 125 use use VERB cana-942 193 126 machine machine NOUN cana-942 193 127 learning learning NOUN cana-942 193 128 into into ADP cana-942 193 129 parameter parameter NOUN cana-942 193 130 simulations simulation NOUN cana-942 193 131 for for ADP cana-942 193 132 water water NOUN cana-942 193 133 quality quality NOUN cana-942 193 134 . . PUNCT cana-942 194 1 to to PART cana-942 194 2 preserve preserve VERB cana-942 194 3 and and CCONJ cana-942 194 4 protect protect VERB cana-942 194 5 coastal coastal ADJ cana-942 194 6 ecosystems ecosystem NOUN cana-942 194 7 , , PUNCT cana-942 194 8 precise precise ADJ cana-942 194 9 prediction prediction NOUN cana-942 194 10 models model NOUN cana-942 194 11 are be AUX cana-942 194 12 crucial crucial ADJ cana-942 194 13 . . PUNCT cana-942 195 1 these these DET cana-942 195 2 models model NOUN cana-942 195 3 must must AUX cana-942 195 4 account account VERB cana-942 195 5 for for ADP cana-942 195 6 the the DET cana-942 195 7 effects effect NOUN cana-942 195 8 of of ADP cana-942 195 9 human human ADJ cana-942 195 10 activities activity NOUN cana-942 195 11 such such ADJ cana-942 195 12 as as ADP cana-942 195 13 urbanization urbanization NOUN cana-942 195 14 , , PUNCT cana-942 195 15 industrialization industrialization NOUN cana-942 195 16 , , PUNCT cana-942 195 17 coastal coastal ADJ cana-942 195 18 reclamation reclamation NOUN cana-942 195 19 , , PUNCT cana-942 195 20 and and CCONJ cana-942 195 21 natural natural ADJ cana-942 195 22 disasters disaster NOUN cana-942 195 23 including include VERB cana-942 195 24 storms storm NOUN cana-942 195 25 , , PUNCT cana-942 195 26 floods flood NOUN cana-942 195 27 , , PUNCT cana-942 195 28 and and CCONJ cana-942 195 29 erosion erosion NOUN cana-942 195 30 . . PUNCT cana-942 196 1 the the DET cana-942 196 2 paper paper NOUN cana-942 196 3 accurately accurately ADV cana-942 196 4 highlights highlight VERB cana-942 196 5 how how SCONJ cana-942 196 6 machine machine NOUN cana-942 196 7 learning learning NOUN cana-942 196 8 plays play VERB cana-942 196 9 a a DET cana-942 196 10 crucial crucial ADJ cana-942 196 11 role role NOUN cana-942 196 12 in in ADP cana-942 196 13 addressing address VERB cana-942 196 14 numerical numerical ADJ cana-942 196 15 models model NOUN cana-942 196 16 ' ' PART cana-942 196 17 shortcomings shortcoming NOUN cana-942 196 18 . . PUNCT cana-942 197 1 problems problem NOUN cana-942 197 2 with with ADP cana-942 197 3 parameter parameter PROPN cana-942 197 4 choice choice NOUN cana-942 197 5 , , PUNCT cana-942 197 6 flexibility flexibility NOUN cana-942 197 7 , , PUNCT cana-942 197 8 and and CCONJ cana-942 197 9 computing computing NOUN cana-942 197 10 efficiency efficiency NOUN cana-942 197 11 plague plague NOUN cana-942 197 12 traditional traditional ADJ cana-942 197 13 models model NOUN cana-942 197 14 . . PUNCT cana-942 198 1 one one NUM cana-942 198 2 potential potential ADJ cana-942 198 3 answer answer NOUN cana-942 198 4 is be AUX cana-942 198 5 the the DET cana-942 198 6 use use NOUN cana-942 198 7 of of ADP cana-942 198 8 machine machine NOUN cana-942 198 9 learning learning NOUN cana-942 198 10 , , PUNCT cana-942 198 11 which which PRON cana-942 198 12 has have AUX cana-942 198 13 been be AUX cana-942 198 14 made make VERB cana-942 198 15 possible possible ADJ cana-942 198 16 by by ADP cana-942 198 17 advancements advancement NOUN cana-942 198 18 in in ADP cana-942 198 19 satellite satellite NOUN cana-942 198 20 remote remote ADJ cana-942 198 21 sensing sensing NOUN cana-942 198 22 and and CCONJ cana-942 198 23 uav uav PROPN cana-942 198 24 observation observation NOUN cana-942 198 25 in in ADP cana-942 198 26 recent recent ADJ cana-942 198 27 times time NOUN cana-942 198 28 . . PUNCT cana-942 199 1 great great ADJ cana-942 199 2 0 0 NUM cana-942 199 3 0.1 0.1 NUM cana-942 199 4 0.2 0.2 NUM cana-942 199 5 0.3 0.3 NUM cana-942 199 6 0.4 0.4 NUM cana-942 199 7 0.5 0.5 NUM cana-942 199 8 0.6 0.6 NUM cana-942 199 9 0.7 0.7 NUM cana-942 199 10 0.8 0.8 NUM cana-942 199 11 svm svm PROPN cana-942 199 12 xgboost xgboost X cana-942 199 13 kneighbours kneighbours PROPN cana-942 199 14 decision decision NOUN cana-942 199 15 tree tree NOUN cana-942 199 16 adaboost adaboost ADV cana-942 199 17 logistic logistic ADJ cana-942 199 18 regression regression NOUN cana-942 199 19 random random ADJ cana-942 199 20 forest forest NOUN cana-942 199 21 classification classification NOUN cana-942 199 22 report report NOUN cana-942 199 23 for for ADP cana-942 199 24 different different ADJ cana-942 199 25 ml ml NOUN cana-942 199 26 algorithm algorithm NOUN cana-942 199 27 accuracy accuracy NOUN cana-942 199 28 classification classification NOUN cana-942 199 29 report report NOUN cana-942 199 30 recall recall NOUN cana-942 199 31 f1score f1score NOUN cana-942 199 32 precision precision NOUN cana-942 199 33 communications communication NOUN cana-942 199 34 on on ADP cana-942 199 35 applied apply VERB cana-942 199 36 nonlinear nonlinear ADJ cana-942 199 37 analysis analysis NOUN cana-942 199 38 issn issn NOUN cana-942 199 39 : : PUNCT cana-942 199 40 1074 1074 NUM cana-942 199 41 - - PUNCT cana-942 199 42 133x 133x NUM cana-942 199 43 vol vol NOUN cana-942 199 44 31 31 NUM cana-942 199 45 no no NOUN cana-942 199 46 . . PUNCT cana-942 200 1 4s 4s NUM cana-942 200 2 ( ( PUNCT cana-942 200 3 2024 2024 NUM cana-942 200 4 ) ) PUNCT cana-942 200 5 461 461 NUM cana-942 200 6 https://internationalpubls.com https://internationalpubls.com X cana-942 200 7 strides stride NOUN cana-942 200 8 have have AUX cana-942 200 9 been be AUX cana-942 200 10 made make VERB cana-942 200 11 in in ADP cana-942 200 12 the the DET cana-942 200 13 processing processing NOUN cana-942 200 14 of of ADP cana-942 200 15 massive massive ADJ cana-942 200 16 datasets dataset NOUN cana-942 200 17 and and CCONJ cana-942 200 18 the the DET cana-942 200 19 extraction extraction NOUN cana-942 200 20 of of ADP cana-942 200 21 relevant relevant ADJ cana-942 200 22 connections connection NOUN cana-942 200 23 between between ADP cana-942 200 24 satellite satellite NOUN cana-942 200 25 images image NOUN cana-942 200 26 and and CCONJ cana-942 200 27 water water NOUN cana-942 200 28 quality quality NOUN cana-942 200 29 measurements measurement NOUN cana-942 200 30 . . PUNCT cana-942 201 1 the the DET cana-942 201 2 significance significance NOUN cana-942 201 3 of of ADP cana-942 201 4 remote remote ADJ cana-942 201 5 sensing sensing NOUN cana-942 201 6 , , PUNCT cana-942 201 7 especially especially ADV cana-942 201 8 satellite satellite NOUN cana-942 201 9 technology technology NOUN cana-942 201 10 , , PUNCT cana-942 201 11 is be AUX cana-942 201 12 brought bring VERB cana-942 201 13 to to ADP cana-942 201 14 light light NOUN cana-942 201 15 in in ADP cana-942 201 16 the the DET cana-942 201 17 examination examination NOUN cana-942 201 18 of of ADP cana-942 201 19 techniques technique NOUN cana-942 201 20 for for ADP cana-942 201 21 collecting collect VERB cana-942 201 22 data datum NOUN cana-942 201 23 on on ADP cana-942 201 24 water water NOUN cana-942 201 25 quality quality NOUN cana-942 201 26 . . PUNCT cana-942 202 1 one one NUM cana-942 202 2 example example NOUN cana-942 202 3 of of ADP cana-942 202 4 an an DET cana-942 202 5 innovative innovative ADJ cana-942 202 6 technique technique NOUN cana-942 202 7 to to ADP cana-942 202 8 water water NOUN cana-942 202 9 quality quality NOUN cana-942 202 10 data data NOUN cana-942 202 11 extraction extraction NOUN cana-942 202 12 is be AUX cana-942 202 13 the the DET cana-942 202 14 use use NOUN cana-942 202 15 of of ADP cana-942 202 16 spectral spectral ADJ cana-942 202 17 reference reference NOUN cana-942 202 18 libraries library NOUN cana-942 202 19 and and CCONJ cana-942 202 20 the the DET cana-942 202 21 investigation investigation NOUN cana-942 202 22 of of ADP cana-942 202 23 optical optical ADJ cana-942 202 24 properties property NOUN cana-942 202 25 . . PUNCT cana-942 203 1 optical optical ADJ cana-942 203 2 feature feature NOUN cana-942 203 3 - - PUNCT cana-942 203 4 trained train VERB cana-942 203 5 machine machine NOUN cana-942 203 6 learning learning NOUN cana-942 203 7 models model NOUN cana-942 203 8 outperform outperform VERB cana-942 203 9 non non ADJ cana-942 203 10 - - ADJ cana-942 203 11 optical optical ADJ cana-942 203 12 indicators indicator NOUN cana-942 203 13 when when SCONJ cana-942 203 14 it it PRON cana-942 203 15 comes come VERB cana-942 203 16 to to ADP cana-942 203 17 forecasting forecast VERB cana-942 203 18 water water NOUN cana-942 203 19 quality quality NOUN cana-942 203 20 metrics metric NOUN cana-942 203 21 . . PUNCT cana-942 204 1 here here ADV cana-942 204 2 , , PUNCT cana-942 204 3 academics academic NOUN cana-942 204 4 and and CCONJ cana-942 204 5 practitioners practitioner NOUN cana-942 204 6 may may AUX cana-942 204 7 find find VERB cana-942 204 8 a a DET cana-942 204 9 precise precise ADJ cana-942 204 10 paradigm paradigm NOUN cana-942 204 11 for for ADP cana-942 204 12 inverting invert VERB cana-942 204 13 water water NOUN cana-942 204 14 quality quality NOUN cana-942 204 15 data datum NOUN cana-942 204 16 from from ADP cana-942 204 17 remote remote ADJ cana-942 204 18 sensing sense VERB cana-942 204 19 maps map NOUN cana-942 204 20 . . PUNCT cana-942 205 1 additionally additionally ADV cana-942 205 2 , , PUNCT cana-942 205 3 this this DET cana-942 205 4 study study NOUN cana-942 205 5 offers offer VERB cana-942 205 6 a a DET cana-942 205 7 thorough thorough ADJ cana-942 205 8 synopsis synopsis NOUN cana-942 205 9 of of ADP cana-942 205 10 machine machine NOUN cana-942 205 11 learning learning NOUN cana-942 205 12 's 's PART cana-942 205 13 uses use NOUN cana-942 205 14 in in ADP cana-942 205 15 forecasting forecast VERB cana-942 205 16 salinity salinity NOUN cana-942 205 17 , , PUNCT cana-942 205 18 dissolved dissolve VERB cana-942 205 19 oxygen oxygen NOUN cana-942 205 20 , , PUNCT cana-942 205 21 chlorophyll chlorophyll NOUN cana-942 205 22 - - PUNCT cana-942 205 23 a a NOUN cana-942 205 24 , , PUNCT cana-942 205 25 and and CCONJ cana-942 205 26 other other ADJ cana-942 205 27 water water NOUN cana-942 205 28 quality quality NOUN cana-942 205 29 metrics metric NOUN cana-942 205 30 . . PUNCT cana-942 206 1 we we PRON cana-942 206 2 provide provide VERB cana-942 206 3 a a DET cana-942 206 4 comprehensive comprehensive ADJ cana-942 206 5 analysis analysis NOUN cana-942 206 6 of of ADP cana-942 206 7 how how SCONJ cana-942 206 8 well well ADV cana-942 206 9 different different ADJ cana-942 206 10 machine machine NOUN cana-942 206 11 learning learn VERB cana-942 206 12 techniques technique NOUN cana-942 206 13 forecast forecast VERB cana-942 206 14 these these DET cana-942 206 15 factors factor NOUN cana-942 206 16 . . PUNCT cana-942 207 1 notably notably ADV cana-942 207 2 , , PUNCT cana-942 207 3 it it PRON cana-942 207 4 has have AUX cana-942 207 5 been be AUX cana-942 207 6 acknowledged acknowledge VERB cana-942 207 7 that that SCONJ cana-942 207 8 data datum NOUN cana-942 207 9 properties property NOUN cana-942 207 10 and and CCONJ cana-942 207 11 local local ADJ cana-942 207 12 circumstances circumstance NOUN cana-942 207 13 dictate dictate VERB cana-942 207 14 the the DET cana-942 207 15 selection selection NOUN cana-942 207 16 of of ADP cana-942 207 17 the the DET cana-942 207 18 most most ADV cana-942 207 19 suitable suitable ADJ cana-942 207 20 algorithm algorithm NOUN cana-942 207 21 . . PUNCT cana-942 208 1 to to PART cana-942 208 2 help help VERB cana-942 208 3 researchers researcher NOUN cana-942 208 4 find find VERB cana-942 208 5 the the DET cana-942 208 6 best good ADJ cana-942 208 7 prediction prediction NOUN cana-942 208 8 models model NOUN cana-942 208 9 , , PUNCT cana-942 208 10 we we PRON cana-942 208 11 have have AUX cana-942 208 12 included include VERB cana-942 208 13 papers paper NOUN cana-942 208 14 that that PRON cana-942 208 15 compare compare VERB cana-942 208 16 various various ADJ cana-942 208 17 methods method NOUN cana-942 208 18 for for ADP cana-942 208 19 certain certain ADJ cana-942 208 20 parameters parameter NOUN cana-942 208 21 . . PUNCT cana-942 209 1 the the DET cana-942 209 2 characteristics characteristic NOUN cana-942 209 3 of of ADP cana-942 209 4 the the DET cana-942 209 5 simulated simulate VERB cana-942 209 6 water water NOUN cana-942 209 7 quality quality NOUN cana-942 209 8 may may AUX cana-942 209 9 be be AUX cana-942 209 10 used use VERB cana-942 209 11 to to PART cana-942 209 12 pick pick VERB cana-942 209 13 from from ADP cana-942 209 14 a a DET cana-942 209 15 variety variety NOUN cana-942 209 16 of of ADP cana-942 209 17 machine machine NOUN cana-942 209 18 learning learn VERB cana-942 209 19 techniques technique NOUN cana-942 209 20 . . PUNCT cana-942 210 1 if if SCONJ cana-942 210 2 you you PRON cana-942 210 3 want want VERB cana-942 210 4 to to PART cana-942 210 5 know know VERB cana-942 210 6 where where SCONJ cana-942 210 7 macro macro ADJ cana-942 210 8 blooms bloom NOUN cana-942 210 9 are be AUX cana-942 210 10 , , PUNCT cana-942 210 11 you you PRON cana-942 210 12 may may AUX cana-942 210 13 use use VERB cana-942 210 14 the the DET cana-942 210 15 classification classification NOUN cana-942 210 16 and and CCONJ cana-942 210 17 regression regression NOUN cana-942 210 18 tree tree NOUN cana-942 210 19 technique technique NOUN cana-942 210 20 [ [ X cana-942 210 21 71 71 NUM cana-942 210 22 ] ] PUNCT cana-942 210 23 . . PUNCT cana-942 211 1 predicting predict VERB cana-942 211 2 levels level NOUN cana-942 211 3 of of ADP cana-942 211 4 escherichia escherichia NOUN cana-942 211 5 coli coli NOUN cana-942 211 6 and and CCONJ cana-942 211 7 enterococci enterococci NOUN cana-942 211 8 may may AUX cana-942 211 9 be be AUX cana-942 211 10 done do VERB cana-942 211 11 using use VERB cana-942 211 12 decision decision NOUN cana-942 211 13 forest forest NOUN cana-942 211 14 , , PUNCT cana-942 211 15 decision decision NOUN cana-942 211 16 jungle jungle NOUN cana-942 211 17 , , PUNCT cana-942 211 18 and and CCONJ cana-942 211 19 boosted boost VERB cana-942 211 20 decision decision NOUN cana-942 211 21 tree tree NOUN cana-942 212 1 [ [ X cana-942 212 2 47 47 NUM cana-942 212 3 ] ] PUNCT cana-942 212 4 . . PUNCT cana-942 213 1 using use VERB cana-942 213 2 extreme extreme ADJ cana-942 213 3 gradient gradient NOUN cana-942 213 4 boosting boost VERB cana-942 213 5 in in ADP cana-942 213 6 conjunction conjunction NOUN cana-942 213 7 with with ADP cana-942 213 8 long long ADJ cana-942 213 9 short short ADJ cana-942 213 10 - - PUNCT cana-942 213 11 term term NOUN cana-942 213 12 memory memory NOUN cana-942 213 13 and and CCONJ cana-942 213 14 support support NOUN cana-942 213 15 vector vector NOUN cana-942 213 16 regression regression NOUN cana-942 213 17 ( ( PUNCT cana-942 213 18 svr svr PROPN cana-942 213 19 ) ) PUNCT cana-942 213 20 on on ADP cana-942 213 21 a a DET cana-942 213 22 single single ADJ cana-942 213 23 model model NOUN cana-942 213 24 for for ADP cana-942 213 25 the the DET cana-942 213 26 purpose purpose NOUN cana-942 213 27 of of ADP cana-942 213 28 calculating calculate VERB cana-942 213 29 chl chl PROPN cana-942 213 30 - - PUNCT cana-942 213 31 a a DET cana-942 213 32 concentrations concentration NOUN cana-942 213 33 , , PUNCT cana-942 213 34 long long ADJ cana-942 213 35 short short ADJ cana-942 213 36 - - PUNCT cana-942 213 37 term term NOUN cana-942 213 38 memory memory NOUN cana-942 213 39 works work VERB cana-942 213 40 better well ADV cana-942 213 41 . . PUNCT cana-942 214 1 predicting predict VERB cana-942 214 2 salinity salinity NOUN cana-942 214 3 concentrations concentration NOUN cana-942 214 4 may may AUX cana-942 214 5 be be AUX cana-942 214 6 done do VERB cana-942 214 7 using use VERB cana-942 214 8 artificial artificial ADJ cana-942 214 9 neural neural ADJ cana-942 214 10 networks network NOUN cana-942 214 11 , , PUNCT cana-942 214 12 gaussian gaussian NOUN cana-942 214 13 processes process NOUN cana-942 214 14 , , PUNCT cana-942 214 15 and and CCONJ cana-942 214 16 support support VERB cana-942 214 17 vector vector NOUN cana-942 214 18 regression regression NOUN cana-942 214 19 . . PUNCT cana-942 215 1 when when SCONJ cana-942 215 2 predicting predict VERB cana-942 215 3 the the DET cana-942 215 4 concentration concentration NOUN cana-942 215 5 of of ADP cana-942 215 6 dissolved dissolved ADJ cana-942 215 7 oxygen oxygen NOUN cana-942 215 8 , , PUNCT cana-942 215 9 random random ADJ cana-942 215 10 forest forest NOUN cana-942 215 11 outperformed outperform VERB cana-942 215 12 support support NOUN cana-942 215 13 vector vector NOUN cana-942 215 14 machine machine NOUN cana-942 215 15 by by ADP cana-942 215 16 a a DET cana-942 215 17 little little ADJ cana-942 215 18 margin margin NOUN cana-942 215 19 . . PUNCT cana-942 216 1 to to PART cana-942 216 2 forecast forecast VERB cana-942 216 3 several several ADJ cana-942 216 4 water water NOUN cana-942 216 5 quality quality NOUN cana-942 216 6 metrics metric NOUN cana-942 216 7 , , PUNCT cana-942 216 8 an an DET cana-942 216 9 ensemble ensemble ADJ cana-942 216 10 machine machine NOUN cana-942 216 11 learning learning NOUN cana-942 216 12 model model NOUN cana-942 216 13 is be AUX cana-942 216 14 an an DET cana-942 216 15 excellent excellent ADJ cana-942 216 16 option option NOUN cana-942 216 17 , , PUNCT cana-942 216 18 as as SCONJ cana-942 216 19 are be AUX cana-942 216 20 support support NOUN cana-942 216 21 vector vector NOUN cana-942 216 22 regression regression NOUN cana-942 216 23 , , PUNCT cana-942 216 24 extreme extreme ADJ cana-942 216 25 gradient gradient NOUN cana-942 216 26 boost boost NOUN cana-942 216 27 , , PUNCT cana-942 216 28 and and CCONJ cana-942 216 29 random random ADJ cana-942 216 30 forest forest NOUN cana-942 216 31 . . PUNCT cana-942 217 1 the the DET cana-942 217 2 random random ADJ cana-942 217 3 forest forest NOUN cana-942 217 4 method method NOUN cana-942 217 5 is be AUX cana-942 217 6 able able ADJ cana-942 217 7 to to PART cana-942 217 8 pick pick VERB cana-942 217 9 important important ADJ cana-942 217 10 water water NOUN cana-942 217 11 quality quality NOUN cana-942 217 12 indicators indicator NOUN cana-942 217 13 , , PUNCT cana-942 217 14 and and CCONJ cana-942 217 15 other other ADJ cana-942 217 16 models model NOUN cana-942 217 17 such such ADJ cana-942 217 18 as as ADP cana-942 217 19 extreme extreme ADJ cana-942 217 20 gradient gradient NOUN cana-942 217 21 boosting boosting NOUN cana-942 217 22 , , PUNCT cana-942 217 23 multi multi ADJ cana-942 217 24 - - ADJ cana-942 217 25 layer layer ADJ cana-942 217 26 perceptron perceptron NOUN cana-942 217 27 , , PUNCT cana-942 217 28 convolutional convolutional ADJ cana-942 217 29 neural neural ADJ cana-942 217 30 network network NOUN cana-942 217 31 , , PUNCT cana-942 217 32 and and CCONJ cana-942 217 33 short short ADJ cana-942 217 34 - - PUNCT cana-942 217 35 term term NOUN cana-942 217 36 memory memory NOUN cana-942 217 37 have have AUX cana-942 217 38 also also ADV cana-942 217 39 shown show VERB cana-942 217 40 strong strong ADJ cana-942 217 41 performance performance NOUN cana-942 217 42 in in ADP cana-942 217 43 forecasting forecasting NOUN cana-942 217 44 wqi wqi NOUN cana-942 217 45 . . PUNCT cana-942 218 1 a a DET cana-942 218 2 subset subset NOUN cana-942 218 3 is be AUX cana-942 218 4 better well ADV cana-942 218 5 predicted predict VERB cana-942 218 6 by by ADP cana-942 218 7 the the DET cana-942 218 8 deep deep ADJ cana-942 218 9 neural neural ADJ cana-942 218 10 network network NOUN cana-942 218 11 technique technique NOUN cana-942 218 12 . . PUNCT cana-942 219 1 one one NUM cana-942 219 2 of of ADP cana-942 219 3 the the DET cana-942 219 4 most most ADV cana-942 219 5 important important ADJ cana-942 219 6 ways way NOUN cana-942 219 7 to to PART cana-942 219 8 measure measure VERB cana-942 219 9 water water NOUN cana-942 219 10 quality quality NOUN cana-942 219 11 is be AUX cana-942 219 12 via via ADP cana-942 219 13 the the DET cana-942 219 14 water water NOUN cana-942 219 15 quality quality NOUN cana-942 219 16 index index NOUN cana-942 219 17 ( ( PUNCT cana-942 219 18 wqi wqi NOUN cana-942 219 19 ) ) PUNCT cana-942 219 20 , , PUNCT cana-942 219 21 and and CCONJ cana-942 219 22 this this DET cana-942 219 23 study study NOUN cana-942 219 24 attempts attempt VERB cana-942 219 25 to to PART cana-942 219 26 clear clear VERB cana-942 219 27 up up ADP cana-942 219 28 some some PRON cana-942 219 29 of of ADP cana-942 219 30 the the DET cana-942 219 31 confusion confusion NOUN cana-942 219 32 around around ADP cana-942 219 33 the the DET cana-942 219 34 limitations limitation NOUN cana-942 219 35 of of ADP cana-942 219 36 older old ADJ cana-942 219 37 wqi wqi ADJ cana-942 219 38 models model NOUN cana-942 219 39 . . PUNCT cana-942 220 1 to to PART cana-942 220 2 improve improve VERB cana-942 220 3 wqi wqi ADJ cana-942 220 4 prediction prediction NOUN cana-942 220 5 , , PUNCT cana-942 220 6 machine machine NOUN cana-942 220 7 learning learning NOUN cana-942 220 8 methods method NOUN cana-942 220 9 including include VERB cana-942 220 10 decision decision NOUN cana-942 220 11 tree tree NOUN cana-942 220 12 , , PUNCT cana-942 220 13 random random ADJ cana-942 220 14 forest forest NOUN cana-942 220 15 , , PUNCT cana-942 220 16 and and CCONJ cana-942 220 17 support support NOUN cana-942 220 18 vector vector NOUN cana-942 220 19 machine machine NOUN cana-942 220 20 have have AUX cana-942 220 21 been be AUX cana-942 220 22 used use VERB cana-942 220 23 . . PUNCT cana-942 221 1 while while SCONJ cana-942 221 2 these these DET cana-942 221 3 strategies strategy NOUN cana-942 221 4 do do AUX cana-942 221 5 provide provide VERB cana-942 221 6 desirable desirable ADJ cana-942 221 7 outcomes outcome NOUN cana-942 221 8 , , PUNCT cana-942 221 9 we we PRON cana-942 221 10 argue argue VERB cana-942 221 11 that that SCONJ cana-942 221 12 they they PRON cana-942 221 13 do do AUX cana-942 221 14 not not PART cana-942 221 15 go go VERB cana-942 221 16 far far ADV cana-942 221 17 enough enough ADV cana-942 221 18 to to PART cana-942 221 19 improve improve VERB cana-942 221 20 wqi wqi VERB cana-942 221 21 in in ADP cana-942 221 22 and and CCONJ cana-942 221 23 of of ADP cana-942 221 24 themselves themselves PRON cana-942 221 25 . . PUNCT cana-942 222 1 we we PRON cana-942 222 2 further further VERB cana-942 222 3 the the DET cana-942 222 4 investigation investigation NOUN cana-942 222 5 of of ADP cana-942 222 6 wqi wqi ADJ cana-942 222 7 prediction prediction NOUN cana-942 222 8 by by ADP cana-942 222 9 talking talk VERB cana-942 222 10 about about ADP cana-942 222 11 continuing continue VERB cana-942 222 12 attempts attempt NOUN cana-942 222 13 to to PART cana-942 222 14 reduce reduce VERB cana-942 222 15 model model NOUN cana-942 222 16 uncertainty uncertainty NOUN cana-942 222 17 and and CCONJ cana-942 222 18 enhance enhance VERB cana-942 222 19 architecture architecture NOUN cana-942 222 20 . . PUNCT cana-942 223 1 a a DET cana-942 223 2 major major ADJ cana-942 223 3 emphasis emphasis NOUN cana-942 223 4 of of ADP cana-942 223 5 this this DET cana-942 223 6 work work NOUN cana-942 223 7 is be AUX cana-942 223 8 the the DET cana-942 223 9 use use NOUN cana-942 223 10 of of ADP cana-942 223 11 hydrodynamics hydrodynamic NOUN cana-942 223 12 into into ADP cana-942 223 13 the the DET cana-942 223 14 process process NOUN cana-942 223 15 of of ADP cana-942 223 16 water water NOUN cana-942 223 17 quality quality NOUN cana-942 223 18 prediction prediction NOUN cana-942 223 19 . . PUNCT cana-942 224 1 a a DET cana-942 224 2 comprehensive comprehensive ADJ cana-942 224 3 strategy strategy NOUN cana-942 224 4 is be AUX cana-942 224 5 required require VERB cana-942 224 6 to to PART cana-942 224 7 address address VERB cana-942 224 8 the the DET cana-942 224 9 issues issue NOUN cana-942 224 10 presented present VERB cana-942 224 11 by by ADP cana-942 224 12 nearshore nearshore PROPN cana-942 224 13 waterways waterways PROPN cana-942 224 14 , , PUNCT cana-942 224 15 which which PRON cana-942 224 16 are be AUX cana-942 224 17 affected affect VERB cana-942 224 18 by by ADP cana-942 224 19 both both PRON cana-942 224 20 coastal coastal ADJ cana-942 224 21 runoff runoff NOUN cana-942 224 22 and and CCONJ cana-942 224 23 oceanic oceanic ADJ cana-942 224 24 pressures pressure NOUN cana-942 224 25 . . PUNCT cana-942 225 1 storm storm NOUN cana-942 225 2 surges surge VERB cana-942 225 3 , , PUNCT cana-942 225 4 wave wave NOUN cana-942 225 5 heights height NOUN cana-942 225 6 , , PUNCT cana-942 225 7 and and CCONJ cana-942 225 8 other other ADJ cana-942 225 9 dynamic dynamic ADJ cana-942 225 10 parameters parameter NOUN cana-942 225 11 may may AUX cana-942 225 12 be be AUX cana-942 225 13 faithfully faithfully ADV cana-942 225 14 predicted predict VERB cana-942 225 15 using use VERB cana-942 225 16 hydrodynamic hydrodynamic ADJ cana-942 225 17 prediction prediction NOUN cana-942 225 18 communications communication NOUN cana-942 225 19 on on ADP cana-942 225 20 applied apply VERB cana-942 225 21 nonlinear nonlinear ADJ cana-942 225 22 analysis analysis NOUN cana-942 225 23 issn issn NOUN cana-942 225 24 : : PUNCT cana-942 225 25 1074 1074 NUM cana-942 225 26 - - PUNCT cana-942 225 27 133x 133x NUM cana-942 225 28 vol vol NOUN cana-942 225 29 31 31 NUM cana-942 225 30 no no NOUN cana-942 225 31 . . PUNCT cana-942 226 1 4s 4s NUM cana-942 226 2 ( ( PUNCT cana-942 226 3 2024 2024 NUM cana-942 226 4 ) ) PUNCT cana-942 226 5 462 462 NUM cana-942 226 6 https://internationalpubls.com https://internationalpubls.com X cana-942 226 7 models model NOUN cana-942 226 8 in in ADP cana-942 226 9 conjunction conjunction NOUN cana-942 226 10 with with ADP cana-942 226 11 machine machine NOUN cana-942 226 12 learning learning NOUN cana-942 226 13 . . PUNCT cana-942 227 1 by by ADP cana-942 227 2 combining combine VERB cana-942 227 3 climate climate NOUN cana-942 227 4 and and CCONJ cana-942 227 5 risk risk NOUN cana-942 227 6 data datum NOUN cana-942 227 7 , , PUNCT cana-942 227 8 our our PRON cana-942 227 9 talk talk NOUN cana-942 227 10 on on ADP cana-942 227 11 the the DET cana-942 227 12 possibilities possibility NOUN cana-942 227 13 of of ADP cana-942 227 14 bayesian bayesian NOUN cana-942 227 15 networks network NOUN cana-942 227 16 in in ADP cana-942 227 17 coastal coastal ADJ cana-942 227 18 risk risk NOUN cana-942 227 19 assessment assessment NOUN cana-942 227 20 offers offer VERB cana-942 227 21 a a DET cana-942 227 22 prospective prospective ADJ cana-942 227 23 view view NOUN cana-942 227 24 on on ADP cana-942 227 25 coastal coastal ADJ cana-942 227 26 water water NOUN cana-942 227 27 quality quality NOUN cana-942 227 28 prediction prediction NOUN cana-942 227 29 . . PUNCT cana-942 228 1 the the DET cana-942 228 2 study study NOUN cana-942 228 3 highlights highlight VERB cana-942 228 4 how how SCONJ cana-942 228 5 machine machine NOUN cana-942 228 6 learning learning NOUN cana-942 228 7 has have AUX cana-942 228 8 revolutionized revolutionize VERB cana-942 228 9 coastal coastal ADJ cana-942 228 10 water water NOUN cana-942 228 11 quality quality NOUN cana-942 228 12 prediction prediction NOUN cana-942 228 13 . . PUNCT cana-942 229 1 our our PRON cana-942 229 2 analysis analysis NOUN cana-942 229 3 of of ADP cana-942 229 4 the the DET cana-942 229 5 shortcomings shortcoming NOUN cana-942 229 6 of of ADP cana-942 229 7 existing exist VERB cana-942 229 8 models model NOUN cana-942 229 9 , , PUNCT cana-942 229 10 the the DET cana-942 229 11 importance importance NOUN cana-942 229 12 of of ADP cana-942 229 13 varied varied ADJ cana-942 229 14 datasets dataset NOUN cana-942 229 15 , , PUNCT cana-942 229 16 and and CCONJ cana-942 229 17 the the DET cana-942 229 18 implications implication NOUN cana-942 229 19 of of ADP cana-942 229 20 changing change VERB cana-942 229 21 environmental environmental ADJ cana-942 229 22 circumstances circumstance NOUN cana-942 229 23 suggests suggest VERB cana-942 229 24 directions direction NOUN cana-942 229 25 for for ADP cana-942 229 26 further further ADJ cana-942 229 27 study study NOUN cana-942 229 28 . . PUNCT cana-942 230 1 8 8 X cana-942 230 2 . . X cana-942 230 3 conclusion conclusion NOUN cana-942 230 4 this this DET cana-942 230 5 study study NOUN cana-942 230 6 summarises summarise VERB cana-942 230 7 all all DET cana-942 230 8 the the DET cana-942 230 9 new new ADJ cana-942 230 10 developments development NOUN cana-942 230 11 in in ADP cana-942 230 12 water water NOUN cana-942 230 13 quality quality NOUN cana-942 230 14 prediction prediction NOUN cana-942 230 15 using use VERB cana-942 230 16 machine machine NOUN cana-942 230 17 learning learning NOUN cana-942 230 18 . . PUNCT cana-942 231 1 it it PRON cana-942 231 2 is be AUX cana-942 231 3 difficult difficult ADJ cana-942 231 4 to to PART cana-942 231 5 choose choose VERB cana-942 231 6 a a DET cana-942 231 7 single single ADJ cana-942 231 8 machine machine NOUN cana-942 231 9 learning learn VERB cana-942 231 10 strategy strategy NOUN cana-942 231 11 with with ADP cana-942 231 12 the the DET cana-942 231 13 highest high ADJ cana-942 231 14 performance performance NOUN cana-942 231 15 , , PUNCT cana-942 231 16 even even ADV cana-942 231 17 after after ADP cana-942 231 18 reviewing review VERB cana-942 231 19 and and CCONJ cana-942 231 20 comparing compare VERB cana-942 231 21 a a DET cana-942 231 22 large large ADJ cana-942 231 23 amount amount NOUN cana-942 231 24 of of ADP cana-942 231 25 literature literature NOUN cana-942 231 26 . . PUNCT cana-942 232 1 machine machine NOUN cana-942 232 2 learning learning NOUN cana-942 232 3 models model NOUN cana-942 232 4 ' ' PART cana-942 232 5 performance performance NOUN cana-942 232 6 might might AUX cana-942 232 7 change change VERB cana-942 232 8 greatly greatly ADV cana-942 232 9 depending depend VERB cana-942 232 10 on on ADP cana-942 232 11 the the DET cana-942 232 12 parameters parameter NOUN cana-942 232 13 and and CCONJ cana-942 232 14 the the DET cana-942 232 15 area area NOUN cana-942 232 16 in in ADP cana-942 232 17 question question NOUN cana-942 232 18 . . PUNCT cana-942 233 1 an an DET cana-942 233 2 encouraging encouraging ADJ cana-942 233 3 direction direction NOUN cana-942 233 4 for for ADP cana-942 233 5 future future ADJ cana-942 233 6 research research NOUN cana-942 233 7 is be AUX cana-942 233 8 to to PART cana-942 233 9 conduct conduct VERB cana-942 233 10 in in ADP cana-942 233 11 - - PUNCT cana-942 233 12 depth depth NOUN cana-942 233 13 analyses analysis NOUN cana-942 233 14 of of ADP cana-942 233 15 the the DET cana-942 233 16 properties property NOUN cana-942 233 17 of of ADP cana-942 233 18 water water NOUN cana-942 233 19 quality quality NOUN cana-942 233 20 parameters parameter NOUN cana-942 233 21 in in ADP cana-942 233 22 an an DET cana-942 233 23 effort effort NOUN cana-942 233 24 to to PART cana-942 233 25 develop develop VERB cana-942 233 26 more more ADV cana-942 233 27 generally generally ADV cana-942 233 28 applicable applicable ADJ cana-942 233 29 methods method NOUN cana-942 233 30 . . PUNCT cana-942 234 1 using use VERB cana-942 234 2 machine machine NOUN cana-942 234 3 learning learning NOUN cana-942 234 4 models model NOUN cana-942 234 5 that that PRON cana-942 234 6 do do AUX cana-942 234 7 n't not PART cana-942 234 8 take take VERB cana-942 234 9 physical physical ADJ cana-942 234 10 and and CCONJ cana-942 234 11 chemical chemical NOUN cana-942 234 12 processes process NOUN cana-942 234 13 into into ADP cana-942 234 14 account account NOUN cana-942 234 15 makes make VERB cana-942 234 16 it it PRON cana-942 234 17 hard hard ADJ cana-942 234 18 to to PART cana-942 234 19 generalize generalize VERB cana-942 234 20 findings finding NOUN cana-942 234 21 for for ADP cana-942 234 22 complex complex ADJ cana-942 234 23 coastal coastal ADJ cana-942 234 24 water water NOUN cana-942 234 25 quality quality NOUN cana-942 234 26 predictions prediction NOUN cana-942 234 27 . . PUNCT cana-942 235 1 predicting predict VERB cana-942 235 2 processes process NOUN cana-942 235 3 incorporating incorporate VERB cana-942 235 4 critical critical ADJ cana-942 235 5 elements element NOUN cana-942 235 6 outside outside ADP cana-942 235 7 the the DET cana-942 235 8 training training NOUN cana-942 235 9 dataset dataset NOUN cana-942 235 10 may may AUX cana-942 235 11 be be AUX cana-942 235 12 beyond beyond ADP cana-942 235 13 the the DET cana-942 235 14 capabilities capability NOUN cana-942 235 15 of of ADP cana-942 235 16 models model NOUN cana-942 235 17 with with ADP cana-942 235 18 regional regional ADJ cana-942 235 19 features feature NOUN cana-942 235 20 . . PUNCT cana-942 236 1 to to PART cana-942 236 2 improve improve VERB cana-942 236 3 machine machine NOUN cana-942 236 4 learning learning NOUN cana-942 236 5 's 's PART cana-942 236 6 prediction prediction NOUN cana-942 236 7 accuracy accuracy NOUN cana-942 236 8 , , PUNCT cana-942 236 9 we we PRON cana-942 236 10 can can AUX cana-942 236 11 do do VERB cana-942 236 12 one one NUM cana-942 236 13 of of ADP cana-942 236 14 two two NUM cana-942 236 15 things thing NOUN cana-942 236 16 : : PUNCT cana-942 236 17 ( ( PUNCT cana-942 236 18 a a X cana-942 236 19 ) ) PUNCT cana-942 236 20 collect collect VERB cana-942 236 21 more more ADJ cana-942 236 22 data datum NOUN cana-942 236 23 from from ADP cana-942 236 24 more more ADJ cana-942 236 25 sources source NOUN cana-942 236 26 and and CCONJ cana-942 236 27 increase increase VERB cana-942 236 28 its its PRON cana-942 236 29 volume volume NOUN cana-942 236 30 ; ; PUNCT cana-942 236 31 ( ( PUNCT cana-942 236 32 b b X cana-942 236 33 ) ) PUNCT cana-942 236 34 fill fill NOUN cana-942 236 35 in in ADP cana-942 236 36 missing miss VERB cana-942 236 37 data datum NOUN cana-942 236 38 using use VERB cana-942 236 39 interpolation interpolation NOUN cana-942 236 40 methods method NOUN cana-942 236 41 like like ADP cana-942 236 42 multiple multiple ADJ cana-942 236 43 - - PUNCT cana-942 236 44 input input NOUN cana-942 236 45 denoising denoising NOUN cana-942 236 46 , , PUNCT cana-942 236 47 k k ADJ cana-942 236 48 - - PUNCT cana-942 236 49 nearest near ADJ cana-942 236 50 neighbors neighbor NOUN cana-942 236 51 ' ' PART cana-942 236 52 input input NOUN cana-942 236 53 , , PUNCT cana-942 236 54 or or CCONJ cana-942 236 55 remote remote ADJ cana-942 236 56 sensing sense VERB cana-942 236 57 satellite satellite NOUN cana-942 236 58 maps map NOUN cana-942 236 59 to to PART cana-942 236 60 invert invert VERB cana-942 236 61 optical optical ADJ cana-942 236 62 characteristic characteristic ADJ cana-942 236 63 parameters parameter NOUN cana-942 236 64 ; ; PUNCT cana-942 236 65 and and CCONJ cana-942 236 66 ( ( PUNCT cana-942 236 67 c c X cana-942 236 68 ) ) PUNCT cana-942 236 69 conduct conduct VERB cana-942 236 70 more more ADJ cana-942 236 71 research research NOUN cana-942 236 72 to to PART cana-942 236 73 quickly quickly ADV cana-942 236 74 and and CCONJ cana-942 236 75 effectively effectively ADV cana-942 236 76 collect collect VERB cana-942 236 77 data datum NOUN cana-942 236 78 on on ADP cana-942 236 79 non non ADJ cana-942 236 80 - - ADJ cana-942 236 81 optical optical ADJ cana-942 236 82 water water NOUN cana-942 236 83 quality quality NOUN cana-942 236 84 parameters parameter NOUN cana-942 236 85 in in ADP cana-942 236 86 coastal coastal ADJ cana-942 236 87 areas area NOUN cana-942 236 88 . . PUNCT cana-942 237 1 researchers researcher NOUN cana-942 237 2 , , PUNCT cana-942 237 3 practitioners practitioner NOUN cana-942 237 4 , , PUNCT cana-942 237 5 and and CCONJ cana-942 237 6 legislators legislator NOUN cana-942 237 7 engaged engage VERB cana-942 237 8 in in ADP cana-942 237 9 environmental environmental ADJ cana-942 237 10 management management NOUN cana-942 237 11 and and CCONJ cana-942 237 12 conservation conservation NOUN cana-942 237 13 will will AUX cana-942 237 14 find find VERB cana-942 237 15 this this DET cana-942 237 16 study study NOUN cana-942 237 17 to to PART cana-942 237 18 be be AUX cana-942 237 19 an an DET cana-942 237 20 invaluable invaluable ADJ cana-942 237 21 resource resource NOUN cana-942 237 22 , , PUNCT cana-942 237 23 as as SCONJ cana-942 237 24 it it PRON cana-942 237 25 provides provide VERB cana-942 237 26 an an DET cana-942 237 27 examination examination NOUN cana-942 237 28 of of ADP cana-942 237 29 the the DET cana-942 237 30 challenges challenge NOUN cana-942 237 31 and and CCONJ cana-942 237 32 developments development NOUN cana-942 237 33 in in ADP cana-942 237 34 coastal coastal ADJ cana-942 237 35 water water NOUN cana-942 237 36 quality quality NOUN cana-942 237 37 prediction prediction NOUN cana-942 237 38 . . PUNCT cana-942 238 1 to to PART cana-942 238 2 better well ADV cana-942 238 3 protect protect VERB cana-942 238 4 the the DET cana-942 238 5 fragile fragile ADJ cana-942 238 6 coastal coastal ADJ cana-942 238 7 ecological ecological ADJ cana-942 238 8 balance balance NOUN cana-942 238 9 , , PUNCT cana-942 238 10 a a DET cana-942 238 11 new new ADJ cana-942 238 12 paradigm paradigm NOUN cana-942 238 13 is be AUX cana-942 238 14 emerging emerge VERB cana-942 238 15 : : PUNCT cana-942 238 16 numerical numerical ADJ cana-942 238 17 simulations simulation NOUN cana-942 238 18 that that PRON cana-942 238 19 include include VERB cana-942 238 20 machine machine NOUN cana-942 238 21 learning learning NOUN cana-942 238 22 . . PUNCT cana-942 239 1 these these DET cana-942 239 2 models model NOUN cana-942 239 3 will will AUX cana-942 239 4 be be AUX cana-942 239 5 more more ADV cana-942 239 6 precise precise ADJ cana-942 239 7 and and CCONJ cana-942 239 8 flexible flexible ADJ cana-942 239 9 . . PUNCT cana-942 240 1 acknowledge acknowledge VERB cana-942 240 2 authors author NOUN cana-942 240 3 acknowledge acknowledge VERB cana-942 240 4 their their PRON cana-942 240 5 parent parent NOUN cana-942 240 6 institutions institution NOUN cana-942 240 7 , , PUNCT cana-942 240 8 [ [ X cana-942 240 9 1 1 X cana-942 240 10 ] ] PUNCT cana-942 240 11 rashtrakavi rashtrakavi VERB cana-942 240 12 ramdhari ramdhari PROPN cana-942 240 13 singh singh PROPN cana-942 240 14 dinkar dinkar PROPN cana-942 240 15 college college PROPN cana-942 240 16 of of ADP cana-942 240 17 engineering engineering NOUN cana-942 240 18 , , PUNCT cana-942 240 19 begusarai begusarai VERB cana-942 240 20 , , PUNCT cana-942 240 21 [ [ X cana-942 240 22 2 2 NUM cana-942 240 23 ] ] PUNCT cana-942 240 24 government government NOUN cana-942 240 25 engineering engineering NOUN cana-942 240 26 college college PROPN cana-942 240 27 , , PUNCT cana-942 240 28 siwan siwan PROPN cana-942 240 29 , , PUNCT cana-942 241 1 [ [ X cana-942 241 2 3 3 NUM cana-942 241 3 ] ] X cana-942 241 4 gaya gaya NOUN cana-942 241 5 college college PROPN cana-942 241 6 of of ADP cana-942 241 7 engineering engineering PROPN cana-942 241 8 , , PUNCT cana-942 241 9 gaya gaya NOUN cana-942 241 10 for for ADP cana-942 241 11 providing provide VERB cana-942 241 12 necessary necessary ADJ cana-942 241 13 resources resource NOUN cana-942 241 14 and and CCONJ cana-942 241 15 research research NOUN cana-942 241 16 support support NOUN cana-942 241 17 . . PUNCT cana-942 242 1 author author NOUN cana-942 242 2 contributions contribution VERB cana-942 242 3 nitya nitya PROPN cana-942 242 4 nand nand PROPN cana-942 242 5 jha jha PROPN cana-942 242 6 . . PROPN cana-942 242 7 , , PUNCT cana-942 242 8 rohit rohit PROPN cana-942 242 9 kumar kumar PROPN cana-942 242 10 singh singh PROPN cana-942 242 11 . . PUNCT cana-942 243 1 and and CCONJ cana-942 243 2 sushila sushila PROPN cana-942 243 3 sharma sharma PROPN cana-942 243 4 . . PUNCT cana-942 243 5 performed perform VERB cana-942 243 6 the the DET cana-942 243 7 measurements measurement NOUN cana-942 243 8 , , PUNCT cana-942 243 9 abhishek abhishek PROPN cana-942 243 10 kumar kumar PROPN cana-942 243 11 was be AUX cana-942 243 12 involved involve VERB cana-942 243 13 in in ADP cana-942 243 14 planning planning NOUN cana-942 243 15 and and CCONJ cana-942 243 16 supervised supervise VERB cana-942 243 17 the the DET cana-942 243 18 work work NOUN cana-942 243 19 . . PUNCT cana-942 244 1 nitya nitya PROPN cana-942 244 2 nand nand PROPN cana-942 244 3 jha jha PROPN cana-942 244 4 . . PROPN cana-942 245 1 and and CCONJ cana-942 245 2 rohit rohit PROPN cana-942 245 3 kumar kumar PROPN cana-942 245 4 singh singh PROPN cana-942 245 5 . . PUNCT cana-942 246 1 processed process VERB cana-942 246 2 the the DET cana-942 246 3 experimental experimental ADJ cana-942 246 4 data datum NOUN cana-942 246 5 , , PUNCT cana-942 246 6 performed perform VERB cana-942 246 7 the the DET cana-942 246 8 analysis analysis NOUN cana-942 246 9 , , PUNCT cana-942 246 10 drafted draft VERB cana-942 246 11 the the DET cana-942 246 12 manuscript manuscript NOUN cana-942 246 13 and and CCONJ cana-942 246 14 designed design VERB cana-942 246 15 the the DET cana-942 246 16 figures figure NOUN cana-942 246 17 . . PUNCT cana-942 247 1 nitya nitya PROPN cana-942 247 2 nand nand PROPN cana-942 247 3 jha jha PROPN cana-942 247 4 . . PROPN cana-942 247 5 , , PUNCT cana-942 247 6 and and CCONJ cana-942 247 7 rohit rohit PROPN cana-942 247 8 kumar kumar PROPN cana-942 247 9 singh singh PROPN cana-942 247 10 . . PUNCT cana-942 248 1 performed perform VERB cana-942 248 2 the the DET cana-942 248 3 calculations calculation NOUN cana-942 248 4 . . PUNCT cana-942 249 1 sushila sushila PROPN cana-942 249 2 sharma sharma PROPN cana-942 249 3 . . PROPN cana-942 249 4 , , PUNCT cana-942 249 5 . . PUNCT cana-942 249 6 manufactured manufacture VERB cana-942 249 7 the the DET cana-942 249 8 samples sample NOUN cana-942 249 9 and and CCONJ cana-942 249 10 characterized characterize VERB cana-942 249 11 them they PRON cana-942 249 12 with with ADP cana-942 249 13 spectroscopy spectroscopy NOUN cana-942 249 14 performed perform VERB cana-942 249 15 the the DET cana-942 249 16 characterization characterization NOUN cana-942 249 17 and and CCONJ cana-942 249 18 abhishek abhishek PROPN cana-942 249 19 kumar kumar PROPN cana-942 249 20 implemented implement VERB cana-942 249 21 all all DET cana-942 249 22 analysis analysis NOUN cana-942 249 23 with with ADP cana-942 249 24 help help NOUN cana-942 249 25 of of ADP cana-942 249 26 machine machine NOUN cana-942 249 27 learning learning NOUN cana-942 249 28 . . PUNCT cana-942 250 1 aided aid VERB cana-942 250 2 in in ADP cana-942 250 3 interpreting interpret VERB cana-942 250 4 the the DET cana-942 250 5 results result NOUN cana-942 250 6 and and CCONJ cana-942 250 7 worked work VERB cana-942 250 8 on on ADP cana-942 250 9 the the DET cana-942 250 10 manuscript manuscript NOUN cana-942 250 11 . . PUNCT cana-942 251 1 all all DET cana-942 251 2 authors author NOUN cana-942 251 3 discussed discuss VERB cana-942 251 4 the the DET cana-942 251 5 results result NOUN cana-942 251 6 and and CCONJ cana-942 251 7 commented comment VERB cana-942 251 8 on on ADP cana-942 251 9 the the DET cana-942 251 10 manuscript manuscript NOUN cana-942 251 11 . . PUNCT cana-942 252 1 communications communication NOUN cana-942 252 2 on on ADP cana-942 252 3 applied apply VERB cana-942 252 4 nonlinear nonlinear ADJ cana-942 252 5 analysis analysis NOUN cana-942 252 6 issn issn NOUN cana-942 252 7 : : PUNCT cana-942 252 8 1074 1074 NUM cana-942 252 9 - - PUNCT cana-942 252 10 133x 133x NUM cana-942 252 11 vol vol NOUN cana-942 252 12 31 31 NUM cana-942 252 13 no no NOUN cana-942 252 14 . . PUNCT cana-942 253 1 4s 4s NUM cana-942 253 2 ( ( PUNCT cana-942 253 3 2024 2024 NUM cana-942 253 4 ) ) PUNCT cana-942 253 5 463 463 NUM cana-942 253 6 https://internationalpubls.com https://internationalpubls.com NUM cana-942 253 7 references reference NOUN cana-942 253 8 [ [ X cana-942 253 9 1 1 NUM cana-942 253 10 ] ] X cana-942 253 11 https://www.kaggle.com/datasets/adityakadiwal/ https://www.kaggle.com/datasets/adityakadiwal/ PROPN cana-942 253 12 water water NOUN cana-942 253 13 - - PUNCT cana-942 253 14 potabilit potabilit NOUN cana-942 253 15 [ [ X cana-942 253 16 2 2 NUM cana-942 253 17 ] ] X cana-942 253 18 patel patel PROPN cana-942 253 19 , , PUNCT cana-942 253 20 j. j. PROPN cana-942 253 21 , , PUNCT cana-942 253 22 amipara amipara PROPN cana-942 253 23 , , PUNCT cana-942 253 24 c. c. NOUN cana-942 253 25 , , PUNCT cana-942 253 26 ahanger ahanger NOUN cana-942 253 27 , , PUNCT cana-942 253 28 t. t. NOUN cana-942 253 29 a. a. PROPN cana-942 253 30 , , PUNCT cana-942 253 31 ladhva ladhva PROPN cana-942 253 32 , , PUNCT cana-942 253 33 k. k. PROPN cana-942 253 34 , , PUNCT cana-942 253 35 gupta gupta PROPN cana-942 253 36 , , PUNCT cana-942 253 37 r. r. PROPN cana-942 253 38 k. k. PROPN cana-942 253 39 , , PUNCT cana-942 253 40 alsaab alsaab PROPN cana-942 253 41 , , PUNCT cana-942 253 42 h. h. PROPN cana-942 253 43 o. o. PROPN cana-942 253 44 , , PUNCT cana-942 253 45 althobaiti althobaiti PROPN cana-942 253 46 , , PUNCT cana-942 253 47 y. y. PROPN cana-942 253 48 s. s. PROPN cana-942 253 49 , , PUNCT cana-942 253 50 & & CCONJ cana-942 253 51 ratna ratna PROPN cana-942 253 52 , , PUNCT cana-942 253 53 r. r. PROPN cana-942 253 54 ( ( PUNCT cana-942 253 55 2022 2022 NUM cana-942 253 56 ) ) PUNCT cana-942 253 57 . . PUNCT cana-942 254 1 a a DET cana-942 254 2 machine machine NOUN cana-942 254 3 learning learning NOUN cana-942 254 4 - - PUNCT cana-942 254 5 based base VERB cana-942 254 6 water water NOUN cana-942 254 7 potability potability NOUN cana-942 254 8 prediction prediction NOUN cana-942 254 9 model model NOUN cana-942 254 10 by by ADP cana-942 254 11 using use VERB cana-942 254 12 synthetic synthetic ADJ cana-942 254 13 minority minority NOUN cana-942 254 14 oversampling oversample VERB cana-942 254 15 technique technique NOUN cana-942 254 16 and and CCONJ cana-942 254 17 explainable explainable ADJ cana-942 254 18 ai ai NOUN cana-942 254 19 . . PUNCT cana-942 254 20 computational computational ADJ cana-942 254 21 intelligence intelligence NOUN cana-942 254 22 and and CCONJ cana-942 254 23 neuroscience neuroscience NOUN cana-942 254 24 , , PUNCT cana-942 254 25 2022 2022 NUM cana-942 254 26 , , PUNCT cana-942 254 27 1–15 1–15 NUM cana-942 254 28 . . PUNCT cana-942 255 1 [ [ X cana-942 255 2 3 3 NUM cana-942 255 3 ] ] X cana-942 255 4 aldhyani aldhyani NOUN cana-942 255 5 , , PUNCT cana-942 255 6 t. t. PROPN cana-942 255 7 h. h. PROPN cana-942 255 8 h. h. PROPN cana-942 255 9 , , PUNCT cana-942 255 10 al al PROPN cana-942 255 11 - - PUNCT cana-942 255 12 yaari yaari PROPN cana-942 255 13 , , PUNCT cana-942 255 14 m. m. NOUN cana-942 255 15 , , PUNCT cana-942 255 16 alkahtani alkahtani PROPN cana-942 255 17 , , PUNCT cana-942 255 18 h. h. PROPN cana-942 255 19 , , PUNCT cana-942 255 20 & & CCONJ cana-942 255 21 maashi maashi PROPN cana-942 255 22 , , PUNCT cana-942 255 23 m. m. NOUN cana-942 255 24 s. s. PROPN cana-942 255 25 ( ( PUNCT cana-942 255 26 2020 2020 NUM cana-942 255 27 ) ) PUNCT cana-942 255 28 . . PUNCT cana-942 256 1 water water NOUN cana-942 256 2 quality quality NOUN cana-942 256 3 prediction prediction NOUN cana-942 256 4 using use VERB cana-942 256 5 artificial artificial ADJ cana-942 256 6 intelligence intelligence NOUN cana-942 256 7 algorithms algorithm NOUN cana-942 256 8 . . PUNCT cana-942 257 1 applied apply VERB cana-942 257 2 bionics bionic NOUN cana-942 257 3 and and CCONJ cana-942 257 4 biomechanics biomechanic NOUN cana-942 257 5 , , PUNCT cana-942 257 6 2020 2020 NUM cana-942 257 7 , , PUNCT cana-942 257 8 1–12 1–12 NOUN cana-942 257 9 . . PUNCT cana-942 258 1 [ [ X cana-942 258 2 4 4 NUM cana-942 258 3 ] ] PUNCT cana-942 258 4 mohammed mohammed PROPN cana-942 258 5 , , PUNCT cana-942 258 6 h. h. PROPN cana-942 258 7 , , PUNCT cana-942 258 8 tornyeviadzi tornyeviadzi PROPN cana-942 258 9 , , PUNCT cana-942 258 10 h. h. PROPN cana-942 258 11 m. m. PROPN cana-942 258 12 , , PUNCT cana-942 258 13 & & CCONJ cana-942 258 14 seidu seidu PROPN cana-942 258 15 , , PUNCT cana-942 258 16 r. r. PROPN cana-942 258 17 ( ( PUNCT cana-942 258 18 2022 2022 NUM cana-942 258 19 ) ) PUNCT cana-942 258 20 . . PUNCT cana-942 259 1 emulating emulate VERB cana-942 259 2 process process NOUN cana-942 259 3 - - PUNCT cana-942 259 4 based base VERB cana-942 259 5 water water NOUN cana-942 259 6 quality quality NOUN cana-942 259 7 modelling modelling NOUN cana-942 259 8 in in ADP cana-942 259 9 water water NOUN cana-942 259 10 source source NOUN cana-942 259 11 reservoirs reservoir NOUN cana-942 259 12 using use VERB cana-942 259 13 machine machine NOUN cana-942 259 14 learning learning NOUN cana-942 259 15 . . PUNCT cana-942 260 1 journal journal PROPN cana-942 260 2 of of ADP cana-942 260 3 hydrology hydrology NOUN cana-942 260 4 , , PUNCT cana-942 260 5 609 609 NUM cana-942 260 6 , , PUNCT cana-942 260 7 127675 127675 NUM cana-942 260 8 . . PUNCT cana-942 261 1 [ [ X cana-942 261 2 5 5 NUM cana-942 261 3 ] ] X cana-942 261 4 peterson peterson PROPN cana-942 261 5 , , PUNCT cana-942 261 6 k. k. PROPN cana-942 261 7 , , PUNCT cana-942 261 8 sidike sidike PROPN cana-942 261 9 , , PUNCT cana-942 261 10 p. p. NOUN cana-942 261 11 , , PUNCT cana-942 261 12 sidike sidike ADJ cana-942 261 13 , , PUNCT cana-942 261 14 p. p. NOUN cana-942 261 15 , , PUNCT cana-942 261 16 hasenmueller hasenmueller PROPN cana-942 261 17 , , PUNCT cana-942 261 18 e. e. PROPN cana-942 261 19 a. a. PROPN cana-942 261 20 , , PUNCT cana-942 261 21 sloan sloan PROPN cana-942 261 22 , , PUNCT cana-942 261 23 j. j. PROPN cana-942 261 24 m. m. PROPN cana-942 261 25 , , PUNCT cana-942 261 26 & & CCONJ cana-942 261 27 knouft knouft PROPN cana-942 261 28 , , PUNCT cana-942 261 29 j. j. PROPN cana-942 261 30 h. h. PROPN cana-942 261 31 ( ( PUNCT cana-942 261 32 2019 2019 NUM cana-942 261 33 ) ) PUNCT cana-942 261 34 . . PUNCT cana-942 262 1 machine machine NOUN cana-942 262 2 learningbased learningbase VERB cana-942 262 3 ensemble ensemble ADJ cana-942 262 4 [ [ X cana-942 262 5 6 6 NUM cana-942 262 6 ] ] SYM cana-942 262 7 fu fu PROPN cana-942 262 8 , , PUNCT cana-942 262 9 zhao zhao NOUN cana-942 262 10 , , PUNCT cana-942 262 11 " " PUNCT cana-942 262 12 water water NOUN cana-942 262 13 quality quality NOUN cana-942 262 14 prediction prediction NOUN cana-942 262 15 based base VERB cana-942 262 16 on on ADP cana-942 262 17 machine machine NOUN cana-942 262 18 learning learning NOUN cana-942 262 19 techniques technique NOUN cana-942 262 20 " " PUNCT cana-942 262 21 ( ( PUNCT cana-942 262 22 2020 2020 NUM cana-942 262 23 ) ) PUNCT cana-942 262 24 . . PUNCT cana-942 263 1 unlv unlv PROPN cana-942 263 2 prediction prediction NOUN cana-942 263 3 of of ADP cana-942 263 4 water water NOUN cana-942 263 5 - - PUNCT cana-942 263 6 quality quality NOUN cana-942 263 7 variables variable NOUN cana-942 263 8 using use VERB cana-942 263 9 feature feature NOUN cana-942 263 10 - - PUNCT cana-942 263 11 level level NOUN cana-942 263 12 and and CCONJ cana-942 263 13 decision decision NOUN cana-942 263 14 - - PUNCT cana-942 263 15 level level NOUN cana-942 263 16 fusion fusion NOUN cana-942 263 17 with with ADP cana-942 263 18 proximal proximal ADJ cana-942 263 19 remote remote ADJ cana-942 263 20 sensing sensing NOUN cana-942 263 21 . . PUNCT cana-942 264 1 photogrammetric photogrammetric ADJ cana-942 264 2 engineering engineering NOUN cana-942 264 3 and and CCONJ cana-942 264 4 remote remote ADJ cana-942 264 5 sensing sensing NOUN cana-942 264 6 , , PUNCT cana-942 264 7 85(4 85(4 NUM cana-942 264 8 ) ) PUNCT cana-942 264 9 , , PUNCT cana-942 264 10 269–280 269–280 NUM cana-942 264 11 . . PUNCT cana-942 265 1 [ [ X cana-942 265 2 7 7 NUM cana-942 265 3 ] ] X cana-942 265 4 kouadri kouadri PROPN cana-942 265 5 , , PUNCT cana-942 265 6 s. s. PROPN cana-942 265 7 , , PUNCT cana-942 265 8 elbeltagi elbeltagi PROPN cana-942 265 9 , , PUNCT cana-942 265 10 a. a. PROPN cana-942 265 11 , , PUNCT cana-942 265 12 islam islam PROPN cana-942 265 13 , , PUNCT cana-942 265 14 a. a. PROPN cana-942 265 15 r. r. PROPN cana-942 265 16 m. m. PROPN cana-942 265 17 t. t. PROPN cana-942 265 18 , , PUNCT cana-942 265 19 & & CCONJ cana-942 265 20 kateb kateb PROPN cana-942 265 21 , , PUNCT cana-942 265 22 s. s. PROPN cana-942 265 23 ( ( PUNCT cana-942 265 24 2021 2021 NUM cana-942 265 25 ) ) PUNCT cana-942 265 26 . . PUNCT cana-942 266 1 performance performance NOUN cana-942 266 2 of of ADP cana-942 266 3 machine machine NOUN cana-942 266 4 learning learning NOUN cana-942 266 5 methods method NOUN cana-942 266 6 in in ADP cana-942 266 7 predicting predict VERB cana-942 266 8 water water NOUN cana-942 266 9 quality quality NOUN cana-942 266 10 index index NOUN cana-942 266 11 based base VERB cana-942 266 12 on on ADP cana-942 266 13 irregular irregular ADJ cana-942 266 14 data datum NOUN cana-942 266 15 set set NOUN cana-942 266 16 : : PUNCT cana-942 266 17 application application NOUN cana-942 266 18 on on ADP cana-942 266 19 illizi illizi ADJ cana-942 266 20 region region NOUN cana-942 266 21 ( ( PUNCT cana-942 266 22 algerian algerian ADJ cana-942 266 23 southeast southeast NOUN cana-942 266 24 ) ) PUNCT cana-942 266 25 . . PUNCT cana-942 267 1 applied apply VERB cana-942 267 2 waterscience,11(12 waterscience,11(12 NOUN cana-942 267 3 ) ) PUNCT cana-942 268 1 [ [ X cana-942 268 2 8 8 NUM cana-942 268 3 ] ] X cana-942 268 4 ahmed ahmed PROPN cana-942 268 5 , , PUNCT cana-942 268 6 u. u. PROPN cana-942 268 7 , , PUNCT cana-942 268 8 mumtaz mumtaz PROPN cana-942 268 9 , , PUNCT cana-942 268 10 r. r. PROPN cana-942 268 11 , , PUNCT cana-942 268 12 anwar anwar PROPN cana-942 268 13 , , PUNCT cana-942 268 14 h. h. PROPN cana-942 268 15 , , PUNCT cana-942 268 16 shah shah PROPN cana-942 268 17 , , PUNCT cana-942 268 18 a. a. NOUN cana-942 268 19 a. a. PROPN cana-942 268 20 , , PUNCT cana-942 268 21 irfan irfan PROPN cana-942 268 22 , , PUNCT cana-942 268 23 r. r. PROPN cana-942 268 24 , , PUNCT cana-942 268 25 & & CCONJ cana-942 268 26 garcΓ­a garcΓ­a PROPN cana-942 268 27 - - PUNCT cana-942 268 28 nieto nieto PROPN cana-942 268 29 , , PUNCT cana-942 268 30 j. j. PROPN cana-942 268 31 ( ( PUNCT cana-942 268 32 2019 2019 NUM cana-942 268 33 ) ) PUNCT cana-942 268 34 . . PUNCT cana-942 269 1 efficient efficient ADJ cana-942 269 2 water water NOUN cana-942 269 3 quality quality NOUN cana-942 269 4 prediction prediction NOUN cana-942 269 5 using use VERB cana-942 269 6 supervised supervised ADJ cana-942 269 7 machine machine NOUN cana-942 269 8 learning learning NOUN cana-942 269 9 . . PUNCT cana-942 270 1 water water NOUN cana-942 270 2 , , PUNCT cana-942 270 3 11(11 11(11 NUM cana-942 270 4 ) ) PUNCT cana-942 270 5 , , PUNCT cana-942 270 6 2210 2210 NUM cana-942 270 7 . . PUNCT cana-942 271 1 [ [ X cana-942 271 2 9 9 NUM cana-942 271 3 ] ] X cana-942 271 4 wang wang PROPN cana-942 271 5 , , PUNCT cana-942 271 6 r. r. PROPN cana-942 271 7 , , PUNCT cana-942 271 8 kim kim PROPN cana-942 271 9 , , PUNCT cana-942 271 10 j. j. PROPN cana-942 271 11 , , PUNCT cana-942 271 12 & & CCONJ cana-942 271 13 li li PROPN cana-942 271 14 , , PUNCT cana-942 271 15 m. m. NOUN cana-942 271 16 ( ( PUNCT cana-942 271 17 2021 2021 NUM cana-942 271 18 ) ) PUNCT cana-942 271 19 . . PUNCT cana-942 272 1 predicting predict VERB cana-942 272 2 stream stream NOUN cana-942 272 3 water water NOUN cana-942 272 4 quality quality NOUN cana-942 272 5 under under ADP cana-942 272 6 different different ADJ cana-942 272 7 urban urban ADJ cana-942 272 8 development development NOUN cana-942 272 9 pattern pattern NOUN cana-942 272 10 scenarios scenario NOUN cana-942 272 11 with with ADP cana-942 272 12 an an DET cana-942 272 13 interpretable interpretable ADJ cana-942 272 14 machine machine NOUN cana-942 272 15 learning learning NOUN cana-942 272 16 approach approach NOUN cana-942 272 17 . . PUNCT cana-942 273 1 science science NOUN cana-942 273 2 of of ADP cana-942 273 3 the the DET cana-942 273 4 total total ADJ cana-942 273 5 environment environment NOUN cana-942 273 6 , , PUNCT cana-942 273 7 761 761 NUM cana-942 273 8 , , PUNCT cana-942 273 9 144057 144057 NUM cana-942 273 10 . . PUNCT cana-942 274 1 [ [ X cana-942 274 2 10 10 NUM cana-942 274 3 ] ] X cana-942 274 4 haghiabi haghiabi NOUN cana-942 274 5 , , PUNCT cana-942 274 6 a. a. PROPN cana-942 274 7 h. h. PROPN cana-942 274 8 , , PUNCT cana-942 274 9 nasrolahi nasrolahi PROPN cana-942 274 10 , , PUNCT cana-942 274 11 a. a. NOUN cana-942 274 12 , , PUNCT cana-942 274 13 & & CCONJ cana-942 274 14 parsaie parsaie PROPN cana-942 274 15 , , PUNCT cana-942 274 16 a. a. NOUN cana-942 274 17 ( ( PUNCT cana-942 274 18 2018 2018 NUM cana-942 274 19 ) ) PUNCT cana-942 274 20 . . PUNCT cana-942 275 1 water water NOUN cana-942 275 2 quality quality NOUN cana-942 275 3 prediction prediction NOUN cana-942 275 4 using use VERB cana-942 275 5 machine machine NOUN cana-942 275 6 learning learning NOUN cana-942 275 7 methods method NOUN cana-942 275 8 . . PUNCT cana-942 276 1 water water NOUN cana-942 276 2 quality quality PROPN cana-942 276 3 research research NOUN cana-942 276 4 journal journal PROPN cana-942 276 5 of of ADP cana-942 276 6 canada canada PROPN cana-942 276 7 , , PUNCT cana-942 276 8 53(1 53(1 PROPN cana-942 276 9 ) ) PUNCT cana-942 276 10 , , PUNCT cana-942 277 1 [ [ X cana-942 277 2 11 11 NUM cana-942 277 3 ] ] X cana-942 277 4 jain jain PROPN cana-942 277 5 d d PROPN cana-942 277 6 , , PUNCT cana-942 277 7 shah shah PROPN cana-942 277 8 s s PROPN cana-942 277 9 , , PUNCT cana-942 277 10 mehta mehta PROPN cana-942 277 11 h h PROPN cana-942 277 12 et et PROPN cana-942 277 13 al al PROPN cana-942 277 14 ( ( PUNCT cana-942 277 15 2021 2021 NUM cana-942 277 16 ) ) PUNCT cana-942 277 17 a a DET cana-942 277 18 machine machine NOUN cana-942 277 19 learning learn VERB cana-942 277 20 approach approach NOUN cana-942 277 21 to to PART cana-942 277 22 analyze analyze VERB cana-942 277 23 marine marine ADJ cana-942 277 24 life life NOUN cana-942 277 25 sustain sustain VERB cana-942 277 26 ability ability NOUN cana-942 277 27 . . PUNCT cana-942 278 1 in in ADP cana-942 278 2 : : PUNCT cana-942 278 3 proceedings proceeding NOUN cana-942 278 4 of of ADP cana-942 278 5 international international ADJ cana-942 278 6 conference conference NOUN cana-942 278 7 on on ADP cana-942 278 8 intelligent intelligent ADJ cana-942 278 9 computing computing NOUN cana-942 278 10 , , PUNCT cana-942 278 11 information information NOUN cana-942 278 12 and and CCONJ cana-942 278 13 control control NOUN cana-942 278 14 systems system NOUN cana-942 278 15 . . PUNCT cana-942 279 1 springer springer NOUN cana-942 279 2 , , PUNCT cana-942 279 3 pp pp PROPN cana-942 280 1 619–632 619–632 NUM cana-942 281 1 [ [ X cana-942 281 2 12 12 NUM cana-942 281 3 ] ] X cana-942 281 4 clark clark PROPN cana-942 281 5 rm rm PROPN cana-942 281 6 , , PUNCT cana-942 281 7 hakim hakim PROPN cana-942 281 8 s s PROPN cana-942 281 9 , , PUNCT cana-942 281 10 ostfeld ostfeld NOUN cana-942 281 11 a a DET cana-942 281 12 ( ( PUNCT cana-942 281 13 2011 2011 NUM cana-942 281 14 ) ) PUNCT cana-942 281 15 handbook handbook NOUN cana-942 281 16 of of ADP cana-942 281 17 water water NOUN cana-942 281 18 and and CCONJ cana-942 281 19 wastewater wastewater NOUN cana-942 281 20 systems system NOUN cana-942 281 21 protection protection NOUN cana-942 281 22 . . PUNCT cana-942 282 1 in in ADP cana-942 282 2 : : PUNCT cana-942 282 3 pro pro X cana-942 282 4 tecting tecte VERB cana-942 282 5 critical critical ADJ cana-942 282 6 infrastructure infrastructure NOUN cana-942 282 7 . . PUNCT cana-942 283 1 springer springer NOUN cana-942 283 2 , , PUNCT cana-942 283 3 pp pp PROPN cana-942 283 4 1–29 1–29 PROPN cana-942 283 5 . . PUNCT cana-942 284 1 https://doi.org/10.1007/978-1-4614-0189-6 https://doi.org/10.1007/978-1-4614-0189-6 PROPN cana-942 285 1 [ [ X cana-942 285 2 13 13 NUM cana-942 285 3 ] ] SYM cana-942 285 4 hu hu PROPN cana-942 286 1 z z PROPN cana-942 286 2 , , PUNCT cana-942 286 3 zhang zhang PROPN cana-942 286 4 y y PROPN cana-942 286 5 , , PUNCT cana-942 286 6 zhao zhao PROPN cana-942 286 7 y y PROPN cana-942 286 8 et et PROPN cana-942 286 9 al al PROPN cana-942 286 10 ( ( PUNCT cana-942 286 11 2019 2019 NUM cana-942 286 12 ) ) PUNCT cana-942 286 13 a a DET cana-942 286 14 water water NOUN cana-942 286 15 quality quality NOUN cana-942 286 16 prediction prediction NOUN cana-942 286 17 method method NOUN cana-942 286 18 based base VERB cana-942 286 19 on on ADP cana-942 286 20 the the DET cana-942 286 21 deep deep ADJ cana-942 286 22 lstm lstm ADJ cana-942 286 23 net net ADJ cana-942 286 24 work work NOUN cana-942 286 25 considering consider VERB cana-942 286 26 correlation correlation NOUN cana-942 286 27 in in ADP cana-942 286 28 smart smart ADJ cana-942 286 29 mariculture mariculture NOUN cana-942 286 30 . . PUNCT cana-942 287 1 sensors sensor NOUN cana-942 287 2 19:1420 19:1420 NUM cana-942 288 1 [ [ X cana-942 288 2 14 14 NUM cana-942 288 3 ] ] X cana-942 288 4 zhou zhou PROPN cana-942 288 5 j j PROPN cana-942 288 6 , , PUNCT cana-942 288 7 wang wang PROPN cana-942 288 8 y y PROPN cana-942 288 9 , , PUNCT cana-942 288 10 xiao xiao PROPN cana-942 288 11 f f PROPN cana-942 288 12 et et PROPN cana-942 288 13 al al PROPN cana-942 288 14 ( ( PUNCT cana-942 288 15 2018 2018 NUM cana-942 288 16 ) ) PUNCT cana-942 288 17 water water NOUN cana-942 288 18 quality quality NOUN cana-942 288 19 prediction prediction NOUN cana-942 288 20 method method NOUN cana-942 288 21 based base VERB cana-942 288 22 on on ADP cana-942 288 23 igra igra PROPN cana-942 288 24 and and CCONJ cana-942 288 25 lstm lstm NOUN cana-942 288 26 . . PUNCT cana-942 289 1 water water NOUN cana-942 289 2 10:1148 10:1148 NUM cana-942 290 1 [ [ X cana-942 290 2 15 15 NUM cana-942 290 3 ] ] X cana-942 290 4 waqas waqas PROPN cana-942 290 5 m m PROPN cana-942 290 6 , , PUNCT cana-942 290 7 tu tu PROPN cana-942 290 8 s s PROPN cana-942 290 9 , , PUNCT cana-942 290 10 halim halim PROPN cana-942 290 11 z z PROPN cana-942 290 12 et et PROPN cana-942 290 13 al al PROPN cana-942 290 14 ( ( PUNCT cana-942 290 15 2022 2022 NUM cana-942 290 16 ) ) PUNCT cana-942 290 17 the the DET cana-942 290 18 role role NOUN cana-942 290 19 of of ADP cana-942 290 20 artificial artificial ADJ cana-942 290 21 intelligence intelligence NOUN cana-942 290 22 and and CCONJ cana-942 290 23 machine machine NOUN cana-942 290 24 learning learn VERB cana-942 290 25 in in ADP cana-942 290 26 wire wire NOUN cana-942 290 27 less less ADJ cana-942 290 28 networks network NOUN cana-942 290 29 security security NOUN cana-942 290 30 : : PUNCT cana-942 290 31 principle principle ADJ cana-942 290 32 , , PUNCT cana-942 290 33 practice practice NOUN cana-942 290 34 and and CCONJ cana-942 290 35 challenges challenge NOUN cana-942 290 36 . . PUNCT cana-942 291 1 artif artif PROPN cana-942 291 2 intell intell PROPN cana-942 291 3 rev rev PROPN cana-942 291 4 55:5215–5261 55:5215–5261 NUM cana-942 291 5 . . PUNCT cana-942 292 1 https://doi https://doi PROPN cana-942 292 2 . . PUNCT cana-942 292 3 org/10.1007 org/10.1007 PROPN cana-942 292 4 / / SYM cana-942 292 5 s10462 s10462 NOUN cana-942 292 6 - - PUNCT cana-942 292 7 02210143 02210143 NUM cana-942 292 8 - - SYM cana-942 292 9 2 2 NUM cana-942 293 1 [ [ X cana-942 293 2 16 16 NUM cana-942 293 3 ] ] X cana-942 293 4 halim halim PROPN cana-942 293 5 z z PROPN cana-942 293 6 , , PUNCT cana-942 293 7 waqar waqar PROPN cana-942 293 8 m m PROPN cana-942 293 9 , , PUNCT cana-942 293 10 tahir tahir PROPN cana-942 293 11 m m PROPN cana-942 293 12 ( ( PUNCT cana-942 293 13 2020 2020 NUM cana-942 293 14 ) ) PUNCT cana-942 293 15 a a DET cana-942 293 16 machine machine NOUN cana-942 293 17 learning learning NOUN cana-942 293 18 - - PUNCT cana-942 293 19 based base VERB cana-942 293 20 investigation investigation NOUN cana-942 293 21 utilizing utilize VERB cana-942 293 22 the the DET cana-942 293 23 in in ADP cana-942 293 24 - - PUNCT cana-942 293 25 text text NOUN cana-942 293 26 fea fea NOUN cana-942 293 27 tures ture NOUN cana-942 293 28 for for ADP cana-942 293 29 the the DET cana-942 293 30 identification identification NOUN cana-942 293 31 of of ADP cana-942 293 32 dominant dominant ADJ cana-942 293 33 emotion emotion NOUN cana-942 293 34 in in ADP cana-942 293 35 an an DET cana-942 293 36 email email NOUN cana-942 293 37 . . PUNCT cana-942 294 1 knowl knowl PROPN cana-942 294 2 based base VERB cana-942 294 3 syst syst PROPN cana-942 294 4 208:106443 208:106443 NUM cana-942 294 5 . . PUNCT cana-942 295 1 https:// https:// PROPN cana-942 295 2 doi.org/10.1016/j.knosys.2020.106443 doi.org/10.1016/j.knosys.2020.106443 PROPN cana-942 296 1 [ [ X cana-942 296 2 17 17 NUM cana-942 296 3 ] ] X cana-942 296 4 wu wu PROPN cana-942 296 5 j j PROPN cana-942 296 6 , , PUNCT cana-942 296 7 wang wang PROPN cana-942 296 8 z z PROPN cana-942 296 9 ( ( PUNCT cana-942 296 10 2022 2022 NUM cana-942 296 11 ) ) PUNCT cana-942 296 12 a a DET cana-942 296 13 hybrid hybrid ADJ cana-942 296 14 model model NOUN cana-942 296 15 for for ADP cana-942 296 16 water water NOUN cana-942 296 17 quality quality NOUN cana-942 296 18 prediction prediction NOUN cana-942 296 19 based base VERB cana-942 296 20 on on ADP cana-942 296 21 an an DET cana-942 296 22 artificial artificial ADJ cana-942 296 23 neural neural ADJ cana-942 296 24 network network NOUN cana-942 296 25 , , PUNCT cana-942 296 26 wavelet wavelet NOUN cana-942 296 27 transform transform NOUN cana-942 296 28 , , PUNCT cana-942 296 29 and and CCONJ cana-942 296 30 long long ADJ cana-942 296 31 short short ADJ cana-942 296 32 - - PUNCT cana-942 296 33 term term NOUN cana-942 296 34 memory memory NOUN cana-942 296 35 . . PUNCT cana-942 297 1 water water NOUN cana-942 297 2 14:610 14:610 NUM cana-942 298 1 [ [ X cana-942 298 2 18 18 NUM cana-942 298 3 ] ] X cana-942 298 4 lee lee PROPN cana-942 298 5 s s PROPN cana-942 298 6 , , PUNCT cana-942 298 7 lee lee PROPN cana-942 298 8 d d PROPN cana-942 298 9 ( ( PUNCT cana-942 298 10 2018 2018 NUM cana-942 298 11 ) ) PUNCT cana-942 298 12 improved improve VERB cana-942 298 13 prediction prediction NOUN cana-942 298 14 of of ADP cana-942 298 15 harmful harmful ADJ cana-942 298 16 algal algal ADJ cana-942 298 17 blooms bloom NOUN cana-942 298 18 in in ADP cana-942 298 19 four four NUM cana-942 298 20 major major ADJ cana-942 298 21 south south PROPN cana-942 298 22 korea korea PROPN cana-942 298 23 ’s ’s PART cana-942 298 24 riv riv PROPN cana-942 298 25 ers er NOUN cana-942 298 26 using use VERB cana-942 298 27 deep deep ADJ cana-942 298 28 learning learning NOUN cana-942 298 29 models model NOUN cana-942 298 30 . . PUNCT cana-942 299 1 int int PROPN cana-942 300 1 j j PROPN cana-942 300 2 environ environ PROPN cana-942 300 3 res res PROPN cana-942 300 4 public public ADJ cana-942 300 5 health health NOUN cana-942 300 6 15:1322 15:1322 PROPN cana-942 301 1 [ [ X cana-942 301 2 19 19 NUM cana-942 301 3 ] ] X cana-942 301 4 liu liu PROPN cana-942 301 5 p p PROPN cana-942 301 6 , , PUNCT cana-942 301 7 wang wang PROPN cana-942 301 8 j j PROPN cana-942 301 9 , , PUNCT cana-942 301 10 sangaiah sangaiah PROPN cana-942 301 11 ak ak PROPN cana-942 301 12 et et PROPN cana-942 301 13 al al PROPN cana-942 301 14 ( ( PUNCT cana-942 301 15 2019 2019 NUM cana-942 301 16 ) ) PUNCT cana-942 301 17 analysis analysis NOUN cana-942 301 18 and and CCONJ cana-942 301 19 prediction prediction NOUN cana-942 301 20 of of ADP cana-942 301 21 water water NOUN cana-942 301 22 quality quality NOUN cana-942 301 23 using use VERB cana-942 301 24 lstm lstm NOUN cana-942 301 25 deep deep ADJ cana-942 301 26 neural neural ADJ cana-942 301 27 networks network NOUN cana-942 301 28 in in ADP cana-942 301 29 iot iot PROPN cana-942 301 30 environment environment NOUN cana-942 301 31 . . PUNCT cana-942 302 1 sustainability sustainability NOUN cana-942 302 2 11:2058 11:2058 NUM cana-942 302 3 10 10 NUM cana-942 302 4 . . PUNCT cana-942 303 1 hmoud hmoud PROPN cana-942 303 2 al al PROPN cana-942 303 3 - - PUNCT cana-942 303 4 adhaileh adhaileh PROPN cana-942 303 5 m m PROPN cana-942 303 6 , , PUNCT cana-942 303 7 waselallah waselallah PROPN cana-942 303 8 alsaade alsaade PROPN cana-942 303 9 f f PROPN cana-942 303 10 ( ( PUNCT cana-942 303 11 2021 2021 NUM cana-942 303 12 ) ) PUNCT cana-942 303 13 modelling modelling NOUN cana-942 303 14 and and CCONJ cana-942 303 15 prediction prediction NOUN cana-942 303 16 of of ADP cana-942 303 17 water water NOUN cana-942 303 18 quality quality NOUN cana-942 303 19 by by ADP cana-942 303 20 using use VERB cana-942 303 21 artificial artificial ADJ cana-942 303 22 intelligence intelligence NOUN cana-942 303 23 . . PUNCT cana-942 304 1 sustainability sustainability NOUN cana-942 304 2 13:4259 13:4259 NUM cana-942 304 3 communications communication NOUN cana-942 304 4 on on ADP cana-942 304 5 applied apply VERB cana-942 304 6 nonlinear nonlinear ADJ cana-942 304 7 analysis analysis NOUN cana-942 304 8 issn issn NOUN cana-942 304 9 : : PUNCT cana-942 304 10 1074 1074 NUM cana-942 304 11 - - PUNCT cana-942 304 12 133x 133x NUM cana-942 304 13 vol vol NOUN cana-942 304 14 31 31 NUM cana-942 304 15 no no NOUN cana-942 304 16 . . PUNCT cana-942 305 1 4s 4s NUM cana-942 305 2 ( ( PUNCT cana-942 305 3 2024 2024 NUM cana-942 305 4 ) ) PUNCT cana-942 305 5 464 464 NUM cana-942 305 6 https://internationalpubls.com https://internationalpubls.com X cana-942 306 1 [ [ X cana-942 306 2 20 20 NUM cana-942 306 3 ] ] X cana-942 306 4 bhardwaj bhardwaj PROPN cana-942 306 5 d d PROPN cana-942 306 6 , , PUNCT cana-942 306 7 verma verma PROPN cana-942 306 8 n n PROPN cana-942 306 9 ( ( PUNCT cana-942 306 10 2017 2017 NUM cana-942 306 11 ) ) PUNCT cana-942 306 12 research research NOUN cana-942 306 13 paper paper NOUN cana-942 306 14 on on ADP cana-942 306 15 analysing analyse VERB cana-942 306 16 impact impact NOUN cana-942 306 17 of of ADP cana-942 306 18 various various ADJ cana-942 306 19 parameters parameter NOUN cana-942 306 20 on on ADP cana-942 306 21 water water NOUN cana-942 306 22 quality quality NOUN cana-942 306 23 index index NOUN cana-942 306 24 . . PUNCT cana-942 307 1 int int PROPN cana-942 308 1 j j PROPN cana-942 308 2 adv adv PROPN cana-942 308 3 res re VERB cana-942 308 4 comput comput ADV cana-942 308 5 sci sci PROPN cana-942 308 6 8(5):2496–498 8(5):2496–498 NUM cana-942 308 7 [ [ X cana-942 308 8 21 21 NUM cana-942 308 9 ] ] X cana-942 308 10 malek malek PROPN cana-942 308 11 nha nha PROPN cana-942 308 12 , , PUNCT cana-942 308 13 wan wan PROPN cana-942 308 14 yaacob yaacob PROPN cana-942 308 15 wf wf PROPN cana-942 308 16 , , PUNCT cana-942 308 17 md md PROPN cana-942 308 18 nasir nasir PROPN cana-942 308 19 sa sa PROPN cana-942 308 20 , , PUNCT cana-942 308 21 shaadan shaadan PROPN cana-942 308 22 n n PROPN cana-942 308 23 ( ( PUNCT cana-942 308 24 2022 2022 NUM cana-942 308 25 ) ) PUNCT cana-942 308 26 prediction prediction NOUN cana-942 308 27 of of ADP cana-942 308 28 water water NOUN cana-942 308 29 quality quality NOUN cana-942 308 30 classi classi PROPN cana-942 308 31 f f PROPN cana-942 308 32 ication ication NOUN cana-942 308 33 of of ADP cana-942 308 34 the the DET cana-942 308 35 kelantan kelantan PROPN cana-942 308 36 river river PROPN cana-942 308 37 basin basin PROPN cana-942 308 38 , , PUNCT cana-942 308 39 malaysia malaysia PROPN cana-942 308 40 , , PUNCT cana-942 308 41 using use VERB cana-942 308 42 machine machine NOUN cana-942 308 43 learning learn VERB cana-942 308 44 techniques technique NOUN cana-942 308 45 . . PUNCT cana-942 309 1 water water NOUN cana-942 309 2 14:1067 14:1067 NUM cana-942 310 1 [ [ X cana-942 310 2 22 22 NUM cana-942 310 3 ] ] PUNCT cana-942 310 4 slatnia slatnia PROPN cana-942 310 5 a a X cana-942 310 6 , , PUNCT cana-942 310 7 ladjal ladjal NOUN cana-942 310 8 m m PROPN cana-942 310 9 , , PUNCT cana-942 310 10 ouali ouali PROPN cana-942 310 11 ma ma PROPN cana-942 310 12 , , PUNCT cana-942 310 13 imed imed PROPN cana-942 310 14 m m PROPN cana-942 310 15 ( ( PUNCT cana-942 310 16 2022 2022 NUM cana-942 310 17 ) ) PUNCT cana-942 310 18 improving improve VERB cana-942 310 19 prediction prediction NOUN cana-942 310 20 and and CCONJ cana-942 310 21 classification classification NOUN cana-942 310 22 of of ADP cana-942 310 23 water water NOUN cana-942 310 24 quality quality NOUN cana-942 310 25 indices index NOUN cana-942 310 26 using use VERB cana-942 310 27 hybrid hybrid ADJ cana-942 310 28 machine machine NOUN cana-942 310 29 learning learn VERB cana-942 310 30 algorithms algorithm NOUN cana-942 310 31 with with ADP cana-942 310 32 features feature NOUN cana-942 310 33 selection selection NOUN cana-942 310 34 analysis analysis NOUN cana-942 310 35 . . PUNCT cana-942 311 1 in in ADP cana-942 311 2 : : PUNCT cana-942 311 3 online online ADJ cana-942 311 4 international international ADJ cana-942 311 5 symposium symposium NOUN cana-942 311 6 on on ADP cana-942 311 7 applied apply VERB cana-942 311 8 mathematics mathematic NOUN cana-942 311 9 and and CCONJ cana-942 311 10 engineering engineering NOUN cana-942 311 11 ( ( PUNCT cana-942 311 12 isame22 isame22 X cana-942 311 13 ) ) PUNCT cana-942 311 14 , , PUNCT cana-942 311 15 vol vol NOUN cana-942 311 16 1 1 NUM cana-942 311 17 . . PUNCT cana-942 311 18 isame22 isame22 PROPN cana-942 311 19 , , PUNCT cana-942 311 20 istanbul istanbul PROPN cana-942 311 21 - - PUNCT cana-942 311 22 turkey turkey NOUN cana-942 311 23 , , PUNCT cana-942 311 24 pp pp PROPN cana-942 311 25 16–17 16–17 PROPN cana-942 312 1 [ [ X cana-942 312 2 23 23 NUM cana-942 312 3 ] ] X cana-942 312 4 deng deng PROPN cana-942 312 5 t t PROPN cana-942 312 6 , , PUNCT cana-942 312 7 chau chau VERB cana-942 312 8 k k PROPN cana-942 312 9 - - PROPN cana-942 312 10 w w PROPN cana-942 312 11 , , PUNCT cana-942 312 12 duan duan PROPN cana-942 312 13 h h PROPN cana-942 312 14 - - PUNCT cana-942 312 15 f f PROPN cana-942 312 16 ( ( PUNCT cana-942 312 17 2021 2021 NUM cana-942 312 18 ) ) PUNCT cana-942 312 19 machine machine NOUN cana-942 312 20 learning learning NOUN cana-942 312 21 based base VERB cana-942 312 22 marine marine ADJ cana-942 312 23 water water NOUN cana-942 312 24 quality quality NOUN cana-942 312 25 prediction prediction NOUN cana-942 312 26 for for ADP cana-942 312 27 coastal coastal ADJ cana-942 312 28 hydroenvironment hydroenvironment NOUN cana-942 312 29 management management NOUN cana-942 312 30 . . PUNCT cana-942 313 1 j j PROPN cana-942 313 2 environ environ PROPN cana-942 313 3 manage manage VERB cana-942 313 4 284:112051 284:112051 PROPN cana-942 313 5 [ [ X cana-942 313 6 24 24 NUM cana-942 313 7 ] ] X cana-942 313 8 khullar khullar PROPN cana-942 313 9 s s PROPN cana-942 313 10 , , PUNCT cana-942 313 11 singh singh ADJ cana-942 313 12 n n CCONJ cana-942 313 13 ( ( PUNCT cana-942 313 14 2022 2022 NUM cana-942 313 15 ) ) PUNCT cana-942 313 16 water water NOUN cana-942 313 17 quality quality NOUN cana-942 313 18 assessment assessment NOUN cana-942 313 19 of of ADP cana-942 313 20 a a DET cana-942 313 21 river river NOUN cana-942 313 22 using use VERB cana-942 313 23 deep deep ADJ cana-942 313 24 learning learn VERB cana-942 313 25 bi bi ADJ cana-942 313 26 - - ADJ cana-942 313 27 lstm lstm ADJ cana-942 313 28 meth meth NOUN cana-942 313 29 odology odology NOUN cana-942 313 30 : : PUNCT cana-942 313 31 forecasting forecasting NOUN cana-942 313 32 and and CCONJ cana-942 313 33 validation validation NOUN cana-942 313 34 . . PUNCT cana-942 314 1 environ environ PROPN cana-942 314 2 sci sci PROPN cana-942 314 3 pollut pollut PROPN cana-942 314 4 res re NOUN cana-942 314 5 29:12875–12889 29:12875–12889 PROPN cana-942 314 6 [ [ X cana-942 314 7 25 25 NUM cana-942 314 8 ] ] X cana-942 314 9 abba abba PROPN cana-942 314 10 si si X cana-942 314 11 , , PUNCT cana-942 314 12 pham pham PROPN cana-942 314 13 qb qb PROPN cana-942 314 14 , , PUNCT cana-942 314 15 saini saini PROPN cana-942 314 16 g g PROPN cana-942 314 17 et et PROPN cana-942 314 18 al al PROPN cana-942 314 19 ( ( PUNCT cana-942 314 20 2020 2020 NUM cana-942 314 21 ) ) PUNCT cana-942 314 22 implementation implementation NOUN cana-942 314 23 of of ADP cana-942 314 24 data datum NOUN cana-942 314 25 intelligence intelligence NOUN cana-942 314 26 models model NOUN cana-942 314 27 coupled couple VERB cana-942 314 28 with with ADP cana-942 314 29 ensem ensem PROPN cana-942 314 30 ble ble PROPN cana-942 314 31 machine machine NOUN cana-942 314 32 learning learn VERB cana-942 314 33 for for ADP cana-942 314 34 prediction prediction NOUN cana-942 314 35 of of ADP cana-942 314 36 water water NOUN cana-942 314 37 quality quality NOUN cana-942 314 38 index index NOUN cana-942 314 39 . . PUNCT cana-942 315 1 environ environ PROPN cana-942 315 2 sci sci PROPN cana-942 315 3 pollut pollut PROPN cana-942 315 4 res re VERB cana-942 315 5 27:41524–41539 27:41524–41539 NUM cana-942 315 6 [ [ X cana-942 315 7 26 26 NUM cana-942 315 8 ] ] PUNCT cana-942 315 9 elbeltagi elbeltagi VERB cana-942 315 10 a a PRON cana-942 315 11 , , PUNCT cana-942 315 12 pande pande PROPN cana-942 315 13 cb cb PROPN cana-942 315 14 , , PUNCT cana-942 315 15 kouadri kouadri PROPN cana-942 315 16 s s PROPN cana-942 315 17 , , PUNCT cana-942 315 18 islam islam PROPN cana-942 315 19 arm arm NOUN cana-942 315 20 ( ( PUNCT cana-942 315 21 2022 2022 NUM cana-942 315 22 ) ) PUNCT cana-942 315 23 applications application NOUN cana-942 315 24 of of ADP cana-942 315 25 various various ADJ cana-942 315 26 data data NOUN cana-942 315 27 - - PUNCT cana-942 315 28 driven drive VERB cana-942 315 29 models model NOUN cana-942 315 30 for for ADP cana-942 315 31 the the DET cana-942 315 32 prediction prediction NOUN cana-942 315 33 of of ADP cana-942 315 34 groundwater groundwater NOUN cana-942 315 35 quality quality NOUN cana-942 315 36 index index NOUN cana-942 315 37 in in ADP cana-942 315 38 the the DET cana-942 315 39 akot akot NOUN cana-942 315 40 basin basin PROPN cana-942 315 41 , , PUNCT cana-942 315 42 maharashtra maharashtra PROPN cana-942 315 43 , , PUNCT cana-942 315 44 india india PROPN cana-942 315 45 . . PUNCT cana-942 316 1 environ environ PROPN cana-942 316 2 sci sci PROPN cana-942 316 3 pollut pollut PROPN cana-942 316 4 res re NOUN cana-942 316 5 29:17591 29:17591 NUM cana-942 316 6 – – PUNCT cana-942 316 7 17605 17605 NUM cana-942 316 8 [ [ X cana-942 316 9 27 27 NUM cana-942 316 10 ] ] PUNCT cana-942 316 11 asadollah asadollah PROPN cana-942 316 12 sbhs sbhs PROPN cana-942 316 13 , , PUNCT cana-942 316 14 sharafati sharafati VERB cana-942 316 15 a a PRON cana-942 316 16 , , PUNCT cana-942 316 17 motta motta PROPN cana-942 316 18 d d PROPN cana-942 316 19 , , PUNCT cana-942 316 20 yaseen yaseen PROPN cana-942 316 21 zm zm PROPN cana-942 316 22 ( ( PUNCT cana-942 316 23 2021 2021 NUM cana-942 316 24 ) ) PUNCT cana-942 316 25 river river NOUN cana-942 316 26 water water NOUN cana-942 316 27 quality quality NOUN cana-942 316 28 index index NOUN cana-942 316 29 prediction prediction NOUN cana-942 316 30 and and CCONJ cana-942 316 31 uncertainty uncertainty NOUN cana-942 316 32 analysis analysis NOUN cana-942 316 33 : : PUNCT cana-942 316 34 a a DET cana-942 316 35 comparative comparative ADJ cana-942 316 36 study study NOUN cana-942 316 37 of of ADP cana-942 316 38 machine machine NOUN cana-942 316 39 learning learning NOUN cana-942 316 40 models model NOUN cana-942 316 41 . . PUNCT cana-942 317 1 j j PROPN cana-942 317 2 environ environ PROPN cana-942 317 3 chem chem PROPN cana-942 317 4 eng eng PROPN cana-942 317 5 9:104599 9:104599 NUM cana-942 318 1 [ [ X cana-942 318 2 28 28 NUM cana-942 318 3 ] ] X cana-942 318 4 nosair nosair NOUN cana-942 318 5 am be AUX cana-942 318 6 , , PUNCT cana-942 318 7 shams sham VERB cana-942 318 8 my my PRON cana-942 318 9 , , PUNCT cana-942 318 10 abouelmagd abouelmagd PROPN cana-942 318 11 lm lm INTJ cana-942 318 12 et et PROPN cana-942 318 13 al al PROPN cana-942 318 14 ( ( PUNCT cana-942 318 15 2022 2022 NUM cana-942 318 16 ) ) PUNCT cana-942 318 17 predictive predictive ADJ cana-942 318 18 model model NOUN cana-942 318 19 for for ADP cana-942 318 20 progressive progressive ADJ cana-942 318 21 saliniza saliniza ADJ cana-942 318 22 tion tion NOUN cana-942 318 23 in in ADP cana-942 318 24 a a DET cana-942 318 25 coastal coastal ADJ cana-942 318 26 aquifer aquifer NOUN cana-942 318 27 using use VERB cana-942 318 28 artificial artificial ADJ cana-942 318 29 intelligence intelligence NOUN cana-942 318 30 and and CCONJ cana-942 318 31 hydrogeochemical hydrogeochemical ADJ cana-942 318 32 techniques technique NOUN cana-942 318 33 : : PUNCT cana-942 318 34 a a DET cana-942 318 35 case case NOUN cana-942 318 36 study study NOUN cana-942 318 37 of of ADP cana-942 318 38 the the DET cana-942 318 39 nile nile PROPN cana-942 318 40 delta delta PROPN cana-942 318 41 aquifer aquifer PROPN cana-942 318 42 , , PUNCT cana-942 318 43 egypt egypt PROPN cana-942 318 44 . . PUNCT cana-942 319 1 environ environ PROPN cana-942 319 2 sci sci PROPN cana-942 319 3 pollut pollut PROPN cana-942 319 4 res re NOUN cana-942 319 5 29:9318–9340 29:9318–9340 NUM cana-942 319 6 [ [ X cana-942 319 7 29 29 NUM cana-942 319 8 ] ] X cana-942 319 9 garabaghi garabaghi PROPN cana-942 319 10 fh fh PROPN cana-942 319 11 , , PUNCT cana-942 319 12 benzer benzer NOUN cana-942 319 13 s s PART cana-942 319 14 , , PUNCT cana-942 319 15 benzer benzer NOUN cana-942 319 16 r r NOUN cana-942 319 17 ( ( PUNCT cana-942 319 18 2021 2021 NUM cana-942 319 19 ) ) PUNCT cana-942 319 20 performance performance NOUN cana-942 319 21 evaluation evaluation NOUN cana-942 319 22 of of ADP cana-942 319 23 machine machine NOUN cana-942 319 24 learning learning NOUN cana-942 319 25 models model NOUN cana-942 319 26 with with ADP cana-942 319 27 ensemble ensemble ADJ cana-942 319 28 learning learning NOUN cana-942 319 29 approach approach NOUN cana-942 319 30 in in ADP cana-942 319 31 classification classification NOUN cana-942 319 32 of of ADP cana-942 319 33 water water NOUN cana-942 319 34 quality quality NOUN cana-942 319 35 indices index NOUN cana-942 319 36 based base VERB cana-942 319 37 on on ADP cana-942 319 38 different different ADJ cana-942 319 39 subset subset NOUN cana-942 319 40 of of ADP cana-942 319 41 features feature NOUN cana-942 319 42 . . PUNCT cana-942 320 1 res re NOUN cana-942 320 2 square square PROPN cana-942 320 3 1:1 1:1 NUM cana-942 320 4 – – PUNCT cana-942 320 5 35 35 NUM cana-942 320 6 . . PUNCT cana-942 321 1 https://doi.org/10.21203/rs.3.rs-876980/v2 https://doi.org/10.21203/rs.3.rs-876980/v2 X cana-942 322 1 [ [ X cana-942 322 2 30 30 NUM cana-942 322 3 ] ] X cana-942 322 4 hassan hassan PROPN cana-942 322 5 mm mm PROPN cana-942 322 6 , , PUNCT cana-942 322 7 hassan hassan PROPN cana-942 322 8 mm mm PROPN cana-942 322 9 , , PUNCT cana-942 322 10 akter akter PROPN cana-942 322 11 l l PROPN cana-942 322 12 et et PROPN cana-942 322 13 al al PROPN cana-942 322 14 ( ( PUNCT cana-942 322 15 2021 2021 NUM cana-942 322 16 ) ) PUNCT cana-942 322 17 efficient efficient ADJ cana-942 322 18 prediction prediction NOUN cana-942 322 19 of of ADP cana-942 322 20 water water NOUN cana-942 322 21 quality quality NOUN cana-942 322 22 index index NOUN cana-942 322 23 ( ( PUNCT cana-942 322 24 wqi wqi VERB cana-942 322 25 ) ) PUNCT cana-942 322 26 using use VERB cana-942 322 27 machine machine NOUN cana-942 322 28 learning learning NOUN cana-942 322 29 algorithms algorithm NOUN cana-942 322 30 . . PUNCT cana-942 323 1 hum hum PROPN cana-942 323 2 centric centric PROPN cana-942 323 3 intell intell PROPN cana-942 323 4 syst syst PROPN cana-942 323 5 1:86–97 1:86–97 PROPN cana-942 324 1 [ [ X cana-942 324 2 31 31 NUM cana-942 324 3 ] ] X cana-942 324 4 radhakrishnan radhakrishnan PROPN cana-942 324 5 n n PROPN cana-942 324 6 , , PUNCT cana-942 324 7 pillai pillai PROPN cana-942 324 8 as as ADP cana-942 324 9 ( ( PUNCT cana-942 324 10 2020 2020 NUM cana-942 324 11 ) ) PUNCT cana-942 324 12 comparison comparison NOUN cana-942 324 13 of of ADP cana-942 324 14 water water NOUN cana-942 324 15 quality quality NOUN cana-942 324 16 classification classification NOUN cana-942 324 17 models model NOUN cana-942 324 18 using use VERB cana-942 324 19 machine machine NOUN cana-942 324 20 learning learning NOUN cana-942 324 21 . . PUNCT cana-942 325 1 in in ADP cana-942 325 2 : : PUNCT cana-942 325 3 2020 2020 NUM cana-942 325 4 5th 5th ADJ cana-942 325 5 international international ADJ cana-942 325 6 conference conference NOUN cana-942 325 7 on on ADP cana-942 325 8 communication communication NOUN cana-942 325 9 and and CCONJ cana-942 325 10 electronics electronics NOUN cana-942 325 11 sys sys NOUN cana-942 325 12 tems tem NOUN cana-942 325 13 ( ( PUNCT cana-942 325 14 icces icce NOUN cana-942 325 15 ) ) PUNCT cana-942 325 16 . . PUNCT cana-942 326 1 ieee ieee NOUN cana-942 326 2 , , PUNCT cana-942 326 3 pp pp ADP cana-942 326 4 1183 1183 NUM cana-942 326 5 – – PUNCT cana-942 326 6 1188 1188 NUM cana-942 326 7 [ [ SYM cana-942 326 8 32 32 NUM cana-942 326 9 ] ] X cana-942 326 10 khan khan PROPN cana-942 326 11 msi msi PROPN cana-942 326 12 , , PUNCT cana-942 326 13 islam islam PROPN cana-942 326 14 n n CCONJ cana-942 326 15 , , PUNCT cana-942 326 16 uddin uddin PROPN cana-942 326 17 j j PROPN cana-942 326 18 et et PROPN cana-942 326 19 al al PROPN cana-942 326 20 ( ( PUNCT cana-942 326 21 2021 2021 NUM cana-942 326 22 ) ) PUNCT cana-942 326 23 water water NOUN cana-942 326 24 quality quality NOUN cana-942 326 25 prediction prediction NOUN cana-942 326 26 and and CCONJ cana-942 326 27 classification classification NOUN cana-942 326 28 based base VERB cana-942 326 29 on on ADP cana-942 326 30 principal principal ADJ cana-942 326 31 component component NOUN cana-942 326 32 regression regression NOUN cana-942 326 33 and and CCONJ cana-942 326 34 gradient gradient NOUN cana-942 326 35 boosting boost VERB cana-942 326 36 classifier classifier NOUN cana-942 326 37 approach approach NOUN cana-942 326 38 . . PUNCT cana-942 327 1 [ [ X cana-942 327 2 33 33 NUM cana-942 327 3 ] ] PUNCT cana-942 327 4 aldhyani aldhyani NOUN cana-942 327 5 thh thh NOUN cana-942 327 6 , , PUNCT cana-942 327 7 al al PROPN cana-942 327 8 - - PUNCT cana-942 327 9 yaari yaari PROPN cana-942 327 10 m m PROPN cana-942 327 11 , , PUNCT cana-942 327 12 alkahtani alkahtani PROPN cana-942 327 13 h h PROPN cana-942 327 14 , , PUNCT cana-942 327 15 maashi maashi PROPN cana-942 327 16 m m PROPN cana-942 327 17 ( ( PUNCT cana-942 327 18 2020 2020 NUM cana-942 327 19 ) ) PUNCT cana-942 327 20 water water NOUN cana-942 327 21 quality quality NOUN cana-942 327 22 prediction prediction NOUN cana-942 327 23 using use VERB cana-942 327 24 artificial artificial ADJ cana-942 327 25 intelligence intelligence NOUN cana-942 327 26 algorithms algorithm NOUN cana-942 327 27 . . PUNCT cana-942 328 1 appl appl NOUN cana-942 328 2 bionics bionics PROPN cana-942 328 3 biomech biomech PROPN cana-942 328 4 2020:1–12 2020:1–12 NUM cana-942 328 5 . . PUNCT cana-942 329 1 https://doi.org/10.1155/2020/6659314 https://doi.org/10.1155/2020/6659314 PROPN cana-942 330 1 [ [ X cana-942 330 2 34 34 NUM cana-942 330 3 ] ] X cana-942 330 4 khoi khoi PROPN cana-942 330 5 dn dn PROPN cana-942 330 6 , , PUNCT cana-942 330 7 quan quan PROPN cana-942 330 8 nt nt PROPN cana-942 330 9 , , PUNCT cana-942 330 10 linh linh INTJ cana-942 330 11 dq dq VERB cana-942 330 12 et et NOUN cana-942 330 13 al al PROPN cana-942 330 14 ( ( PUNCT cana-942 330 15 2022 2022 NUM cana-942 330 16 ) ) PUNCT cana-942 330 17 using use VERB cana-942 330 18 machine machine NOUN cana-942 330 19 learning learning NOUN cana-942 330 20 models model NOUN cana-942 330 21 for for ADP cana-942 330 22 predicting predict VERB cana-942 330 23 the the DET cana-942 330 24 water water NOUN cana-942 330 25 quality quality NOUN cana-942 330 26 index index NOUN cana-942 330 27 in in ADP cana-942 330 28 the the DET cana-942 330 29 la la PROPN cana-942 330 30 buong buong PROPN cana-942 330 31 river river PROPN cana-942 330 32 , , PUNCT cana-942 330 33 vietnam vietnam PROPN cana-942 330 34 . . PUNCT cana-942 331 1 water water NOUN cana-942 331 2 14:155 14:155 NUM cana-942 332 1 [ [ X cana-942 332 2 35 35 NUM cana-942 332 3 ] ] PUNCT cana-942 332 4 forests forest VERB cana-942 332 5 r r NOUN cana-942 332 6 , , PUNCT cana-942 332 7 breiman breiman NOUN cana-942 332 8 l l PROPN cana-942 332 9 ( ( PUNCT cana-942 332 10 1999 1999 NUM cana-942 332 11 ) ) PUNCT cana-942 332 12 statistics statistics PROPN cana-942 332 13 department department PROPN cana-942 332 14 university university PROPN cana-942 332 15 of of ADP cana-942 332 16 california california PROPN cana-942 332 17 berkeley berkeley PROPN cana-942 332 18 . . PUNCT cana-942 333 1 pp pp ADP cana-942 333 2 1 1 NUM cana-942 333 3 - - SYM cana-942 333 4 29 29 NUM cana-942 333 5 27 27 NUM cana-942 333 6 . . PUNCT cana-942 334 1 biau biau PROPN cana-942 334 2 g g PROPN cana-942 334 3 ( ( PUNCT cana-942 334 4 2012 2012 NUM cana-942 334 5 ) ) PUNCT cana-942 334 6 analysis analysis NOUN cana-942 334 7 of of ADP cana-942 334 8 a a DET cana-942 334 9 random random ADJ cana-942 334 10 forests forest NOUN cana-942 334 11 model model NOUN cana-942 334 12 . . PUNCT cana-942 335 1 j j PROPN cana-942 335 2 mach mach NOUN cana-942 335 3 learn learn VERB cana-942 335 4 res re NOUN cana-942 335 5 13:1063–1095 13:1063–1095 NUM cana-942 335 6 [ [ X cana-942 335 7 36 36 NUM cana-942 335 8 ] ] X cana-942 335 9 wang wang PROPN cana-942 335 10 s s PROPN cana-942 335 11 , , PUNCT cana-942 335 12 peng peng PROPN cana-942 335 13 h h PROPN cana-942 335 14 , , PUNCT cana-942 335 15 liang liang PROPN cana-942 335 16 s s X cana-942 335 17 ( ( PUNCT cana-942 335 18 2022 2022 NUM cana-942 335 19 ) ) PUNCT cana-942 335 20 prediction prediction NOUN cana-942 335 21 of of ADP cana-942 335 22 estuarine estuarine NOUN cana-942 335 23 water water NOUN cana-942 335 24 quality quality NOUN cana-942 335 25 using use VERB cana-942 335 26 interpretable interpretable ADJ cana-942 335 27 machine machine NOUN cana-942 335 28 learning learning NOUN cana-942 335 29 approach approach NOUN cana-942 335 30 . . PUNCT cana-942 336 1 j j PROPN cana-942 336 2 hydrol hydrol VERB cana-942 336 3 605:127320 605:127320 PROPN cana-942 337 1 [ [ X cana-942 337 2 37 37 NUM cana-942 337 3 ] ] X cana-942 337 4 chen chen PROPN cana-942 337 5 t t PROPN cana-942 337 6 , , PUNCT cana-942 337 7 guestrin guestrin PROPN cana-942 337 8 c c PROPN cana-942 337 9 ( ( PUNCT cana-942 337 10 2016 2016 NUM cana-942 337 11 ) ) PUNCT cana-942 337 12 xgboost xgboost ADV cana-942 337 13 : : PUNCT cana-942 337 14 a a DET cana-942 337 15 scalable scalable ADJ cana-942 337 16 tree tree NOUN cana-942 337 17 boosting boost VERB cana-942 337 18 system system NOUN cana-942 337 19 . . PUNCT cana-942 338 1 in in ADP cana-942 338 2 : : PUNCT cana-942 338 3 proceedings proceeding NOUN cana-942 338 4 of of ADP cana-942 338 5 the the DET cana-942 338 6 22nd 22nd PROPN cana-942 338 7 acm acm PROPN cana-942 338 8 sigkdd sigkdd PROPN cana-942 338 9 international international ADJ cana-942 338 10 conference conference NOUN cana-942 338 11 on on ADP cana-942 338 12 knowledge knowledge NOUN cana-942 338 13 discovery discovery PROPN cana-942 338 14 and and CCONJ cana-942 338 15 data datum NOUN cana-942 338 16 mining mining NOUN cana-942 338 17 . . PUNCT cana-942 339 1 pp pp ADP cana-942 340 1 785–794 785–794 NUM cana-942 340 2 [ [ PUNCT cana-942 340 3 38 38 NUM cana-942 340 4 ] ] X cana-942 340 5 prakash prakash PROPN cana-942 340 6 r r PROPN cana-942 340 7 , , PUNCT cana-942 340 8 tharun tharun NOUN cana-942 340 9 vp vp PROPN cana-942 340 10 , , PUNCT cana-942 340 11 devi devi PROPN cana-942 340 12 sr sr PROPN cana-942 340 13 ( ( PUNCT cana-942 340 14 2018 2018 NUM cana-942 340 15 ) ) PUNCT cana-942 340 16 a a DET cana-942 340 17 comparative comparative ADJ cana-942 340 18 study study NOUN cana-942 340 19 of of ADP cana-942 340 20 various various ADJ cana-942 340 21 classification classification NOUN cana-942 340 22 techniques technique NOUN cana-942 340 23 to to PART cana-942 340 24 determine determine VERB cana-942 340 25 water water NOUN cana-942 340 26 quality quality NOUN cana-942 340 27 . . PUNCT cana-942 341 1 in in ADP cana-942 341 2 : : PUNCT cana-942 341 3 2018 2018 NUM cana-942 341 4 second second ADJ cana-942 341 5 international international ADJ cana-942 341 6 conference conference NOUN cana-942 341 7 on on ADP cana-942 341 8 inventive inventive ADJ cana-942 341 9 communication communication NOUN cana-942 341 10 and and CCONJ cana-942 341 11 computational computational ADJ cana-942 341 12 technologies technology NOUN cana-942 341 13 ( ( PUNCT cana-942 341 14 icicct icicct ADJ cana-942 341 15 ) ) PUNCT cana-942 341 16 . . PUNCT cana-942 342 1 ieee ieee NOUN cana-942 342 2 , , PUNCT cana-942 342 3 pp pp ADP cana-942 342 4 1501–1506 1501–1506 NOUN cana-942 342 5 [ [ X cana-942 342 6 39 39 NUM cana-942 342 7 ] ] PUNCT cana-942 342 8 friedman friedman PROPN cana-942 342 9 jh jh PROPN cana-942 342 10 ( ( PUNCT cana-942 342 11 2002 2002 NUM cana-942 342 12 ) ) PUNCT cana-942 342 13 stochastic stochastic ADJ cana-942 342 14 gradient gradient NOUN cana-942 342 15 boosting boosting NOUN cana-942 342 16 . . PUNCT cana-942 343 1 comput comput PROPN cana-942 343 2 stat stat PROPN cana-942 343 3 data data PROPN cana-942 343 4 anal anal PROPN cana-942 343 5 38:367–378 38:367–378 PROPN cana-942 343 6 communications communication NOUN cana-942 343 7 on on ADP cana-942 343 8 applied apply VERB cana-942 343 9 nonlinear nonlinear ADJ cana-942 343 10 analysis analysis NOUN cana-942 343 11 issn issn NOUN cana-942 343 12 : : PUNCT cana-942 343 13 1074 1074 NUM cana-942 343 14 - - PUNCT cana-942 343 15 133x 133x NUM cana-942 343 16 vol vol NOUN cana-942 343 17 31 31 NUM cana-942 343 18 no no NOUN cana-942 343 19 . . PUNCT cana-942 344 1 4s 4s NUM cana-942 344 2 ( ( PUNCT cana-942 344 3 2024 2024 NUM cana-942 344 4 ) ) PUNCT cana-942 344 5 465 465 NUM cana-942 344 6 https://internationalpubls.com https://internationalpubls.com X cana-942 345 1 [ [ X cana-942 345 2 40 40 NUM cana-942 345 3 ] ] PUNCT cana-942 345 4 zhou zhou PROPN cana-942 345 5 y y PROPN cana-942 345 6 , , PUNCT cana-942 345 7 mazzuchi mazzuchi VERB cana-942 345 8 ta ta X cana-942 345 9 , , PUNCT cana-942 345 10 sarkani sarkani PROPN cana-942 345 11 s s PART cana-942 345 12 ( ( PUNCT cana-942 345 13 2020 2020 NUM cana-942 345 14 ) ) PUNCT cana-942 345 15 m m PROPN cana-942 345 16 - - PUNCT cana-942 345 17 adaboost adaboost ADV cana-942 345 18 - - PUNCT cana-942 345 19 a a DET cana-942 345 20 based base VERB cana-942 345 21 ensemble ensemble ADJ cana-942 345 22 system system NOUN cana-942 345 23 for for ADP cana-942 345 24 network network NOUN cana-942 345 25 intru intru PROPN cana-942 345 26 sion sion PROPN cana-942 345 27 detection detection NOUN cana-942 345 28 . . PUNCT cana-942 346 1 expert expert NOUN cana-942 346 2 syst syst PROPN cana-942 346 3 appl appl PROPN cana-942 346 4 162:113864 162:113864 PROPN cana-942 347 1 [ [ X cana-942 347 2 41 41 NUM cana-942 347 3 ] ] X cana-942 347 4 beyer beyer PROPN cana-942 347 5 k k PROPN cana-942 347 6 , , PUNCT cana-942 347 7 goldstein goldstein PROPN cana-942 347 8 j j PROPN cana-942 347 9 , , PUNCT cana-942 347 10 ramakrishnan ramakrishnan NOUN cana-942 347 11 r r NOUN cana-942 347 12 , , PUNCT cana-942 347 13 shaft shaft NOUN cana-942 347 14 u u NOUN cana-942 347 15 ( ( PUNCT cana-942 347 16 1999 1999 NUM cana-942 347 17 ) ) PUNCT cana-942 347 18 when when SCONJ cana-942 347 19 is be AUX cana-942 347 20 β€œ " PUNCT cana-942 347 21 nearest near ADJ cana-942 347 22 neighbor neighbor NOUN cana-942 347 23 ” " PUNCT cana-942 347 24 meaningful meaningful ADJ cana-942 347 25 ? ? PUNCT cana-942 348 1 in in ADP cana-942 348 2 : : PUNCT cana-942 348 3 international international ADJ cana-942 348 4 conference conference NOUN cana-942 348 5 on on ADP cana-942 348 6 database database PROPN cana-942 348 7 theory theory PROPN cana-942 348 8 . . PUNCT cana-942 349 1 springer springer NOUN cana-942 349 2 , , PUNCT cana-942 349 3 pp pp PROPN cana-942 349 4 217–235 217–235 NUM cana-942 349 5 [ [ X cana-942 349 6 42 42 NUM cana-942 349 7 ] ] X cana-942 349 8 lu lu PROPN cana-942 349 9 h h NOUN cana-942 349 10 , , PUNCT cana-942 349 11 ma ma PROPN cana-942 349 12 x x X cana-942 349 13 ( ( PUNCT cana-942 349 14 2020 2020 NUM cana-942 349 15 ) ) PUNCT cana-942 349 16 hybrid hybrid ADJ cana-942 349 17 decision decision NOUN cana-942 349 18 tree tree NOUN cana-942 349 19 - - PUNCT cana-942 349 20 based base VERB cana-942 349 21 machine machine NOUN cana-942 349 22 learning learning NOUN cana-942 349 23 models model NOUN cana-942 349 24 for for ADP cana-942 349 25 short short ADJ cana-942 349 26 - - PUNCT cana-942 349 27 term term NOUN cana-942 349 28 water water NOUN cana-942 349 29 quality quality NOUN cana-942 349 30 prediction prediction NOUN cana-942 349 31 . . PUNCT cana-942 350 1 chemosphere chemosphere VERB cana-942 350 2 249:126169 249:126169 NUM cana-942 351 1 [ [ X cana-942 351 2 43 43 NUM cana-942 351 3 ] ] X cana-942 351 4 halim halim PROPN cana-942 351 5 z z PROPN cana-942 351 6 , , PUNCT cana-942 351 7 rehan rehan PROPN cana-942 351 8 m m PROPN cana-942 351 9 ( ( PUNCT cana-942 351 10 2020 2020 NUM cana-942 351 11 ) ) PUNCT cana-942 351 12 on on ADP cana-942 351 13 identification identification NOUN cana-942 351 14 of of ADP cana-942 351 15 driving drive VERB cana-942 351 16 - - PUNCT cana-942 351 17 induced induce VERB cana-942 351 18 stress stress NOUN cana-942 351 19 using use VERB cana-942 351 20 electroencephalogram electroencephalogram NOUN cana-942 351 21 sig sig NOUN cana-942 351 22 nals nal NOUN cana-942 351 23 : : PUNCT cana-942 351 24 a a DET cana-942 351 25 framework framework NOUN cana-942 351 26 based base VERB cana-942 351 27 on on ADP cana-942 351 28 wearable wearable ADJ cana-942 351 29 safety safety NOUN cana-942 351 30 - - PUNCT cana-942 351 31 critical critical ADJ cana-942 351 32 scheme scheme NOUN cana-942 351 33 and and CCONJ cana-942 351 34 machine machine NOUN cana-942 351 35 learning learning NOUN cana-942 351 36 . . PUNCT cana-942 352 1 inf inf ADJ cana-942 352 2 fusion fusion NOUN cana-942 352 3 53:66 53:66 NUM cana-942 352 4 79 79 NUM cana-942 352 5 . . PUNCT cana-942 353 1 https://doi.org/10.1016/j.inffus.2019.06.006 https://doi.org/10.1016/j.inffus.2019.06.006 NOUN cana-942 353 2 [ [ PUNCT cana-942 353 3 44 44 NUM cana-942 353 4 ] ] PUNCT cana-942 353 5 chen chen PROPN cana-942 353 6 h h PROPN cana-942 353 7 , , PUNCT cana-942 353 8 huang huang PROPN cana-942 353 9 jj jj PROPN cana-942 353 10 , , PUNCT cana-942 353 11 mcbean mcbean ADJ cana-942 353 12 e e X cana-942 353 13 ( ( PUNCT cana-942 353 14 2020 2020 NUM cana-942 353 15 ) ) PUNCT cana-942 353 16 partitioning partitioning NOUN cana-942 353 17 of of ADP cana-942 353 18 daily daily ADJ cana-942 353 19 evapotranspiration evapotranspiration NOUN cana-942 353 20 using use VERB cana-942 353 21 a a DET cana-942 353 22 modified modify VERB cana-942 353 23 shut shut NOUN cana-942 353 24 tleworthwallace tleworthwallace NOUN cana-942 353 25 model model NOUN cana-942 353 26 , , PUNCT cana-942 353 27 random random ADJ cana-942 353 28 forest forest NOUN cana-942 353 29 and and CCONJ cana-942 353 30 support support VERB cana-942 353 31 vector vector NOUN cana-942 353 32 regression regression NOUN cana-942 353 33 , , PUNCT cana-942 353 34 for for ADP cana-942 353 35 a a DET cana-942 353 36 cabbage cabbage NOUN cana-942 353 37 farmland farmland NOUN cana-942 353 38 . . PUNCT cana-942 354 1 agric agric ADJ cana-942 354 2 water water PROPN cana-942 354 3 manag manag NOUN cana-942 354 4 228:105923 228:105923 NUM cana-942 355 1 [ [ X cana-942 355 2 45 45 NUM cana-942 355 3 ] ] X cana-942 355 4 cheng cheng PROPN cana-942 355 5 y y PROPN cana-942 355 6 , , PUNCT cana-942 355 7 peng peng PROPN cana-942 355 8 j j PROPN cana-942 355 9 , , PUNCT cana-942 355 10 gu gu NOUN cana-942 355 11 x x PROPN cana-942 355 12 et et PROPN cana-942 355 13 al al PROPN cana-942 355 14 ( ( PUNCT cana-942 355 15 2020 2020 NUM cana-942 355 16 ) ) PUNCT cana-942 355 17 an an DET cana-942 355 18 intelligent intelligent ADJ cana-942 355 19 supplier supplier NOUN cana-942 355 20 evaluation evaluation NOUN cana-942 355 21 model model NOUN cana-942 355 22 based base VERB cana-942 355 23 on on ADP cana-942 355 24 data data NOUN cana-942 355 25 - - PUNCT cana-942 355 26 driven drive VERB cana-942 355 27 support support NOUN cana-942 355 28 vector vector NOUN cana-942 355 29 regression regression NOUN cana-942 355 30 in in ADP cana-942 355 31 global global ADJ cana-942 355 32 supply supply NOUN cana-942 355 33 chain chain NOUN cana-942 355 34 . . PUNCT cana-942 356 1 comput comput PROPN cana-942 356 2 ind ind PROPN cana-942 356 3 eng eng PROPN cana-942 356 4 139:105834 139:105834 NUM cana-942 357 1 [ [ X cana-942 357 2 46 46 NUM cana-942 357 3 ] ] X cana-942 357 4 liao liao PROPN cana-942 357 5 z z PROPN cana-942 357 6 , , PUNCT cana-942 357 7 li li PROPN cana-942 357 8 y y PROPN cana-942 357 9 , , PUNCT cana-942 357 10 xiong xiong PROPN cana-942 357 11 w w PROPN cana-942 357 12 et et PROPN cana-942 357 13 al al PROPN cana-942 357 14 ( ( PUNCT cana-942 357 15 2020 2020 NUM cana-942 357 16 ) ) PUNCT cana-942 357 17 an an DET cana-942 357 18 in in ADP cana-942 357 19 - - PUNCT cana-942 357 20 depth depth NOUN cana-942 357 21 assessment assessment NOUN cana-942 357 22 of of ADP cana-942 357 23 water water NOUN cana-942 357 24 resource resource NOUN cana-942 357 25 responses response NOUN cana-942 357 26 to to ADP cana-942 357 27 regional regional ADJ cana-942 357 28 development development NOUN cana-942 357 29 policies policy NOUN cana-942 357 30 using use VERB cana-942 357 31 hydrological hydrological ADJ cana-942 357 32 variation variation NOUN cana-942 357 33 analysis analysis NOUN cana-942 357 34 and and CCONJ cana-942 357 35 system system NOUN cana-942 357 36 dynamics dynamic NOUN cana-942 357 37 modeling modeling NOUN cana-942 357 38 . . PUNCT cana-942 358 1 sus sus PROPN cana-942 358 2 tainability tainability NOUN cana-942 358 3 12:5814 12:5814 NUM cana-942 359 1 [ [ X cana-942 359 2 47 47 NUM cana-942 359 3 ] ] PUNCT cana-942 359 4 tyagi tyagi PROPN cana-942 359 5 s s PROPN cana-942 359 6 , , PUNCT cana-942 359 7 sharma sharma PROPN cana-942 359 8 b b PROPN cana-942 359 9 , , PUNCT cana-942 359 10 singh singh PROPN cana-942 359 11 p p NOUN cana-942 359 12 , , PUNCT cana-942 359 13 dobhal dobhal NOUN cana-942 359 14 r r NOUN cana-942 359 15 ( ( PUNCT cana-942 359 16 2013 2013 NUM cana-942 359 17 ) ) PUNCT cana-942 359 18 water water NOUN cana-942 359 19 quality quality NOUN cana-942 359 20 assessment assessment NOUN cana-942 359 21 in in ADP cana-942 359 22 terms term NOUN cana-942 359 23 of of ADP cana-942 359 24 water water NOUN cana-942 359 25 quality quality NOUN cana-942 359 26 index index NOUN cana-942 359 27 . . PUNCT cana-942 360 1 am be AUX cana-942 360 2 j j PROPN cana-942 360 3 water water NOUN cana-942 360 4 resour resour NOUN cana-942 360 5 1:34–38 1:34–38 PROPN cana-942 361 1 [ [ X cana-942 361 2 48 48 NUM cana-942 361 3 ] ] PUNCT cana-942 361 4 shams sham VERB cana-942 361 5 my my PRON cana-942 361 6 , , PUNCT cana-942 361 7 tarek tarek PROPN cana-942 361 8 z z PROPN cana-942 361 9 , , PUNCT cana-942 361 10 elshewey elshewey PROPN cana-942 361 11 am be AUX cana-942 361 12 et et PROPN cana-942 361 13 al al PROPN cana-942 361 14 ( ( PUNCT cana-942 361 15 2023 2023 NUM cana-942 361 16 ) ) PUNCT cana-942 361 17 a a DET cana-942 361 18 machine machine NOUN cana-942 361 19 learning learning NOUN cana-942 361 20 - - PUNCT cana-942 361 21 based base VERB cana-942 361 22 model model NOUN cana-942 361 23 for for ADP cana-942 361 24 predicting predict VERB cana-942 361 25 temperature temperature NOUN cana-942 361 26 under under ADP cana-942 361 27 the the DET cana-942 361 28 effects effect NOUN cana-942 361 29 of of ADP cana-942 361 30 climate climate NOUN cana-942 361 31 change change NOUN cana-942 361 32 . . PUNCT cana-942 362 1 in in ADP cana-942 362 2 : : PUNCT cana-942 362 3 hassanien hassanien PROPN cana-942 362 4 ae ae PROPN cana-942 362 5 , , PUNCT cana-942 362 6 darwish darwish VERB cana-942 362 7 a a DET cana-942 362 8 ( ( PUNCT cana-942 362 9 eds ed NOUN cana-942 362 10 ) ) PUNCT cana-942 362 11 the the DET cana-942 362 12 power power NOUN cana-942 362 13 of of ADP cana-942 362 14 data datum NOUN cana-942 362 15 : : PUNCT cana-942 362 16 driving drive VERB cana-942 362 17 climate climate NOUN cana-942 362 18 change change NOUN cana-942 362 19 with with ADP cana-942 362 20 data datum NOUN cana-942 362 21 science science NOUN cana-942 362 22 and and CCONJ cana-942 362 23 artificial artificial ADJ cana-942 362 24 intelligence intelligence NOUN cana-942 362 25 innovations innovation NOUN cana-942 362 26 . . PUNCT cana-942 363 1 springer springer NOUN cana-942 363 2 nature nature PROPN cana-942 363 3 switzerland switzerland PROPN cana-942 363 4 , , PUNCT cana-942 363 5 cham cham PROPN cana-942 363 6 , , PUNCT cana-942 363 7 pp pp ADP cana-942 363 8 61–81 61–81 NUM cana-942 363 9 [ [ SYM cana-942 363 10 49 49 NUM cana-942 363 11 ] ] PUNCT cana-942 363 12 elshewey elshewey PROPN cana-942 363 13 am be AUX cana-942 363 14 , , PUNCT cana-942 363 15 shams sham VERB cana-942 363 16 my my PRON cana-942 363 17 , , PUNCT cana-942 363 18 elhady elhady PROPN cana-942 363 19 am be AUX cana-942 363 20 et et PROPN cana-942 363 21 al al PROPN cana-942 363 22 ( ( PUNCT cana-942 363 23 2023 2023 NUM cana-942 363 24 ) ) PUNCT cana-942 363 25 a a DET cana-942 363 26 novel novel ADJ cana-942 363 27 wd wd ADJ cana-942 363 28 - - PUNCT cana-942 363 29 sarimax sarimax ADJ cana-942 363 30 model model NOUN cana-942 363 31 for for ADP cana-942 363 32 temperature temperature NOUN cana-942 363 33 forecasting forecasting NOUN cana-942 363 34 using use VERB cana-942 363 35 daily daily ADJ cana-942 363 36 delhi delhi ADJ cana-942 363 37 climate climate NOUN cana-942 363 38 dataset dataset NOUN cana-942 363 39 . . PUNCT cana-942 364 1 sustainability sustainability NOUN cana-942 364 2 15:757 15:757 NUM cana-942 364 3 . . PUNCT cana-942 365 1 https://doi.org/10.3390/su15010757 https://doi.org/10.3390/su15010757 X cana-942 365 2 42 42 NUM cana-942 365 3 . . PUNCT cana-942 366 1 tarek tarek PROPN cana-942 366 2 z z PROPN cana-942 366 3 , , PUNCT cana-942 366 4 shams sham VERB cana-942 366 5 my my PRON cana-942 366 6 , , PUNCT cana-942 366 7 elshewey elshewey PROPN cana-942 366 8 am be AUX cana-942 366 9 et et PROPN cana-942 366 10 al al PROPN cana-942 366 11 ( ( PUNCT cana-942 366 12 2023 2023 NUM cana-942 366 13 ) ) PUNCT cana-942 366 14 wind wind NOUN cana-942 366 15 power power NOUN cana-942 366 16 prediction prediction NOUN cana-942 366 17 based base VERB cana-942 366 18 on on ADP cana-942 366 19 machine machine NOUN cana-942 366 20 learning learning NOUN cana-942 366 21 and and CCONJ cana-942 366 22 deep deep ADJ cana-942 366 23 learning learning NOUN cana-942 366 24 models model NOUN cana-942 366 25 . . PUNCT cana-942 367 1 comput comput NOUN cana-942 367 2 mater mater NOUN cana-942 367 3 contin contin X cana-942 367 4 74:715–732.https://doi.org/10.32604 74:715–732.https://doi.org/10.32604 NUM cana-942 367 5 / / SYM cana-942 367 6 cmc.2023.032533 cmc.2023.032533 NUM cana-942 367 7 [ [ SYM cana-942 367 8 50 50 NUM cana-942 367 9 ] ] PUNCT cana-942 367 10 elshewey elshewey PROPN cana-942 367 11 am be AUX cana-942 367 12 , , PUNCT cana-942 367 13 shams sham VERB cana-942 367 14 my my PRON cana-942 367 15 , , PUNCT cana-942 367 16 tarek tarek PROPN cana-942 367 17 z z PROPN cana-942 367 18 et et PROPN cana-942 367 19 al al PROPN cana-942 367 20 ( ( PUNCT cana-942 367 21 2023 2023 NUM cana-942 367 22 ) ) PUNCT cana-942 367 23 weight weight NOUN cana-942 367 24 prediction prediction NOUN cana-942 367 25 using use VERB cana-942 367 26 the the DET cana-942 367 27 hybrid hybrid ADJ cana-942 367 28 stacked stack VERB cana-942 367 29 - - PUNCT cana-942 367 30 lstm lstm NOUN cana-942 367 31 food food NOUN cana-942 367 32 selection selection NOUN cana-942 367 33 . . PUNCT