







































Georgian Geographical Journal 

 

Supervised Machine Learning in Drought 

Evaluation 
Ana Palavandishvili1,2    
1 TSU, Vakhushti Bagrationi Institute of Geography, Tbilisi, Georgia 
2 

Department of Engineering Physics/Informatics and Control Systems, GTU, 

Tbilisi, Georgia
 

* Corresponding author: ana.palavandishvili@tsu.ge  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Introduction 

Big Data is a rapidly generated amount of information from a variety of sources and in a different 

format. Data analysis is the examination and transformation of raw data into interpretable information, 

while data science is a multidisciplinary field of various analyses, programming tools, and algorithms, 

forecasting analysis statistics, as well as machine learning that aim to recognize and extract patterns in 

raw data. The applicability of big data techniques is also significantly enhanced by the novel tools that 

support data collection and integration. The interoperability of the systems can be improved by data 

warehouses and the related ETL (extract, transform, and load) functionalities that can also be used to 

gather information from multiple models and data sources. Artificial intelligence (AI) and machine 

learning (ML) are also the key enabler technologies of big data analysis (Tatishvili et al., 2022a). 

Analysis of Big Data combines traditional methods of statistical analysis with computational 

Georgian Geographical Journal, 2024, 4(2) 30-37 

© The Author(s) 2024 

 
This article is an open access article distributed under 

the terms and conditions of the Creative Commons 

Attribution (CC BY) license (https:// 

creativecommons.org/licenses/by/ 4.0/). 

DOI: 

https://journals.4science.ge/index.php/GGJ 

Abstract 

Among natural disasters, drought is one of the most common threats to many 

regions of the world and to Georgia as well. The monitoring and prediction methods 

of drought and precipitation distribution, the possibilities of their application in the 

reality of Georgia are considered in proposed work. Simulation methods such as 

Machine Learning (ML), namely Supervised Machine Learning (SML), optimal 

for similar complex tasks are presented as the alternative research methods. To 

conduct research, 1960-2022 period data were taken from database of National 

Environment Agency and the reanalysis data of the 1960-1990 Copernicus ERA5 

rainfall, which were compared with the data of the stations on the territory of 

Georgia for the validation purpose. Standardized Precipitation Index (SPI) was 

selected as the research parameter. Using the prediction model and algorithm, 

drought-vulnerable areas in the Kakheti region were identified. As a result of 

comparison, lowest correlation rate was 0.309 at Shiraki, maximum was 0.657 at 

Omalo; minimum mean absolute error 1,662 at Udabno, the maximum 3,041 in 

Shilda. The smallest standard deviation 4,047 was fixes at Udabno, largest 7,624 at 

Lagodekhi. By analyzing stations data and satellite sources, it was determined that 

using the regression method of Machine Learning, it is sufficient to evaluate 1960-

2000 period data for learning and 2001-2022 period data for training. The training 

time of Bagged Trees Optimized Algorithm was recorded as 326.21 sec, prediction 

speed ~ 7900obs/sec, RMSE - 0.5046, R2-0.64, MSE-0.25466, MAE-0.38065, 

training process minimum leaf size 19, and 40 iterations are assigned for 

optimization. CHIRPS satellite data were taken for next generation of the model. 

For prediction, it was necessary to calculate linear regression equation for each 

station. In the first case of forecast scenario, the amount of precipitation was 

determined from 0 cm to 10 cm. Gurjaani was highlighted, where forecast assumed 

SPI value from -0.008 to -0.901, and Kvareli- the SPI value from -0.002 to -0.138.  

The use of the presented ML model and algorithm for the analysis of precipitation 

distribution, drought monitoring and prediction is appropriate for Kakheti and other 

regions too in conditions of proper observation data base (60 years). It is 

recommended to use obtained results in early warning system for drought 

monitoring. 

Keywords: Drought, Machine Learning, Big Data, early warning system. 

Citation: Palavandishvili, A. Supervised 

Machine Learning in Drought Evaluation. 
Georgian Geographical Journal 2024, 

4(2). 30-37 

https://doi.org/10.52340/ggj.2024.04.02.04 



Palavandishvili. 2024 4(2) 

31 
 

approaches. The analysis of big data is a synthesis of quantitative and qualitative analyses. Climate 

computing combines multidisciplinary research regarding climatic, data, and system sciences to 

efficiently capture and analyse climate-related big data as well as to support socio-environmental efforts 

(Tatishvili et al., 2022). The significance of big data in climate-related studies is greatly recognized, 

and its techniques are widely used to observe and monitor changes on a global scale. It facilitates 

understanding and forecasting to support adaptive decision-making as well as optimize models and 

structures. 

Drought is a climatic event that cannot be prevented, but interventions and preparedness to drought 

can help to: be better prepared to cope with drought; develop more resilient ecosystems; improve 

resilience to recover from drought; and mitigate the impacts of droughts. 

Methods and Materials 

Drought indices have been developed in large numbers and are widely used in drought evaluation, 

monitoring, and forecasting. Machine learning is a subset of artificial intelligence. Machine learning 

(ML) algorithms are a set of commands that allow systems to learn and improve from prior data without 

requiring complex programming. ML techniques have been used to implement prediction or forecasting 

of drought. These algorithms work by simulating a model from input datasets known as test sets and 

then using the model findings to forecast, predict, or make various types of judgments in various 

application domains. Various machine learning techniques are extensively used in the prediction of 

drought. 

In order to conduct research, available data from the NEA database is used. Unfortunately, databases 

aren’t perfect: data series aren’t continuous and are less reliable, which is a big problem for index 

calculation and machine learning. Therefore, ERA5 reanalysis data were selected for historical data. 

Based on the comparison of satellite CHIPRS and IMERG information, it was determined that CHIRPS 

has a good correlation with the data of the stations in the territory of Georgia (Tatishvili et al., 2023). 

Those stations where 50% of the data were missing or did not correspond to reality were not subjected 

to analysis. Als, the satellite cannot perceive the mountainous region; the sea is not subject to 

observation because a filter (mask) has been applied, so in many cases it is impossible to obtain satellite 

data on the coastline; in this case, a nearby grid box of another grid must be selected. 

Figure 1 shows a list of stations with insufficient data and whose data were not subjected to statistical 

analysis, and Figure 2 shows the results of the inventory of all (50) stations made using the program R-

instat and covers the 2000-2020 period. 

Figure 1 shows a list of stations with insufficient data and whose data were not subjected to statistical 

analysis 

A comparison of CHIRPS and station data was made, for which the systematic error (BIAS) was 

calculated, which refers to the estimation of the difference between the monthly totals of precipitation 

measured from the satellite and the ground station. 

Results 

Figure 1. 2000 - 2020 list of stations that were not subjected to statistical analysis. 



Palavandishvili. 2024 4(2) 

32 
 

Figure 2 shows the results of the inventory of all (50) stations made using the program R-instat and 

covers the period 2000-2020. The SPEI index was calculated for 1960 – 2022 period using the software 

package R—programming language Climpact2. Because data on daily precipitation and maximum and 

minimum temperature are needed to calculate this index, sufficient data for calculations were found for 

only 4 stations out of 17. As for the calculation of SPI, the total precipitation data of each month is 

required; the data of 10 stations that have at least 40 years of data series have been subjected to the 

analysis. 

When calculating the three-month SPI, the index for the first January and February of the time series 

does not exist; to recover it, it is necessary to take the average of the month of January or February of 

each year of the entire series (Tatishvili et al., 2022b). 

Correlation for the observations entire period, for all months and years, mean absolute errors and 

standard deviation were also calculated according to the same principle. The counting results are 

presented in Table 1. 

Table 1. Calculated statistical parameters 

Station Correlation 

Mean 

absolute 

error 

Standard 

deviation 

 

Correlation 

SPI3month_CHIRPS 

 

Correlation 

IMERG 

SPI_3month 

Correlation SPI3 

month _CHIRPS 

check 

Akhalqalaqi 0.66759 28.7 26.84318 0.416248 0.291970887 0.443102744 

Akhaltsikhe 0.460294 38.1 33.55621 0.352573 0.484119499 0.352572913 

Batumi Airport 0.58213 83.2 112.5929 0.448736 0.340454562 0.448736396 

Borjomi 0.641079 24.1 25.26509 0.244318 0.310999227 0.244318316 

Chokhatauri 0.578691 46.6 66.39357 -0.07615 0.165519382 -0.076151789 

Gori 0.600696 18.8 23.40047 0.374053 0.424890006 0.374053213 

Khashuri 0.602859 20.7 25.35372 0.384062 0.356057737 0.384061993 

Manglisi - - - -  - 

Mta-Sabueti 0.331451 41.3 51.98547 0.300716 0.31919611 0.300716118 

Mukhrani 0.670568 22.5 24.8989 0.235464 -0.102300227 0.235464432 

Pasanauri 0.633913 33.4 42.5678 0.290633 0.309023193 0.290632874 

Poti 0.456032 72.8 111.9462 0.344086 0.375124979 0.344085861 

Qeda - - - -  - 

Qobuleti 0.623388 77.5 102.4052 0.286832 0.363684022 0.286831685 

Qutaisi 0.66483 35.2 45.62226 0.483474 0.201479561 0.483474128 

Sachkhere 0.592227 26.6 34.78904 0.27458 0.441969055 0.27458026 

Sagarejo 0.619239 27.7 37.34099 0.076687 0.233896552 0.076687323 

Senaki 0.647958 47.3 60.19786 0.747709 0.544157721 0.747709077 

Shovi 0.717298 30.0 36.37001 0.440142 0.34740251 0.440141859 

Stephantsminda - - - -  - 

Tbilisi 0.678032 21.1 28.35372 0.208955 0.236983184 0.208954519 

Telavi 0.693299 26.0 35.45118 0.271435 0.362963042 0.271434581 

Tianeti 0.588555 22.0 31.88968 0.191648 0.321092386 0.191647776 

Figure 2. Inventory of 50 stations, the absence of data is marked in red, brown - dry 

period, blue - precipitation 



Palavandishvili. 2024 4(2) 

33 
 

Tsalka 0.504015 32.4 39.38712 0.285891 0.353743633 0.285890812 

Zugdidi 0.603897 51.9 62.13524 0.477354 0.417972195 0.477354223 

 

The calculations showed that in those stations where more than 50% of the data from the observation 

period are missing, for example, in Manglis, it is impossible to conduct a statistical analysis. A total of 

nine such stations were identified. From the analysis of the calculation of statistical characteristics, it is 

clear that the lowest correlation values are 0.33 at the Mta-Sabueti station and the maximum is 0.72 at 

the Shovi station; the minimum average absolute error is 18.8 at the Gori station and the maximum - 

83.2 at the Batumi station; the smallest standard deviation is 23.4 at the Gori station; and the largest - 

118.46 at Mirveti station, which is understandable considering the coastal and mountainous terrain. A 

high correlation coefficient better expresses the agreement between satellite and station data. 

R - instat software was used to calculate Pearson's correlation and other statistical parameters. Stations 

that were omitted could not be analyzed. 

Historical precipitation data from 1961–1991 were compared with ERA5 precipitation reanalysis data. 

19 stations were selected for the Kakheti region. Of course, those points where there was insufficient 

data were not subjected to the analysis (Tatishvili et al., 2022c). 

In the result of the data comparison, the lowest correlation index (0.309) was revealed at the Shiraki 

station, the maximum (0.657) at the Omalo station. The minimum average absolute error (1.662) was 

recorded at Udabno station, the maximum (3.041) at Shielda station. The smallest standard deviation 

(4.047) is at Udabno station, the largest (7.624) at Lagodekhi station. By comparing the historical and 

ERA5 reanalysis data of the station for 1961 – 1991 period, reanalysis data can be used as an alternative 

database for the model, both in Kakheti and in the entire territory of Georgia. 

Table 2. Comparison of station and ERA5 reanalysis precipitation1961-1991 data 

Stations Standard deviation Correlation Mean abs. error 

Akhmeta 5.76054943 0.5936142 2.22960019 

Artana 6.81349292 0.58776933 2.34396992 

Birkiani 7.62295759 0.63601722 2.50106601 

Dedoplistskaro 5.177286 0.57295248 2.34935663 

Gombori 5.48090201 0.57076494 2.1249113 

Gurjaani 6.29609913 0.61040494 2.13628114 

Kachreti 5.37111452 0.5144723 2.64170272 

Khvareli 7.51038719 0.58870058 2.30064283 

Lagodekhi 7.62411052 0.55838778 2.39522353 

Lechuri 7.26083196 0.48469664 2.65489445 

Omalo 5.06645709 0.65703807 2.28134972 

Sabue 7.18089762 0.5644877 2.44340144 

Sagarejo 6.09202268 0.60668778 2.295695 

Shilda 6.35923532 0.48506866 3.04148853 

Shiraki 5.45504798 0.30906783 2.58658036 

Tsiauri 4.83284442 0.5361194 1.87135863 

Telavi 6.16741437 0.61484533 2.13956842 

Tsnori 5.19081531 0.62222577 1.96229826 

Udabno 4.04739384 0.51336395 1.66217209 

     

Discussions 

At the initial stage of machine learning, the station coordinates, station name, year, month, monthly 

precipitation total, and 3-month precipitation index were included in the training (Mastering Machine 

Learning). A 30-year observation period was analysed at all. As a result of counting, we got a good 

result only for those stations that are located close to each other and the overfit for the stations located 

at a relatively long distance, e.g., Omalo, Akhmeta. 



Palavandishvili. 2024 4(2) 

34 
 

 

Figure 3. Comparison of the model created by the SVM optimized algorithm with the real data. True data values are marked 

in blue and values generated by the model in yellow 

The parameters for estimating the model generated by the support vector machine (SVM) of the first 

model are: RMSE - 0.58848, R2 _0.64, MSE_0.34631, MAE_0.45479, prediction speed ~430000 

obs/sec (observations per second), training time - 30,296 s, minimum leaf size - 45 (optimized 

parameter), hyperparametric search ranking: minimum leaf size_1-3420. All these parameters are 

needed to estimate the model. To avoid excessive adjustment, 30 additional stations in the Kartli region 

were added. 

 
Figure 4. Comparison of the model generated by MatLab and the actual observation data 



Palavandishvili. 2024 4(2) 

35 
 

Figure 4 shows a visual comparison of the actual and predicted results generated by MatLab. Actual 

data value is marked in blue, predicted data value in yellow. 

Bagged trees assembled by an optimized algorithm showed the best results in regression algorithm 

training. Learning time is 326.21 s, prediction speed - ~ 7900 observations/second, characteristic 

parameters: RMSE - 0.5046, R2 - 0.64, MSE - 0.25466, MAE - 0.38065. 

 

Figure 5. Prediction response generated by MatLab 

Figure 5 shows the observation points and the best forecast. Based on the analysis of this drawing, we 

can judge what kind of model it is. If the data (points) are very scattered, this indicates underfitting, 

while points that are close to the prediction curve (line) indicate overfitting (McHanay, 2014). When 

many points are gathered in one area, it can be concluded that the model is a good fit, i.e., satisfactory. 

 

 

Figure 6. Learning process of optimized Bagged Trees generated by MatLab 



Palavandishvili. 2024 4(2) 

36 
 

Figure 6 depicts the optimized Bagged Trees learning process. The minimum leaf size is 19, with 40 

iterations instead of 30 iterations assigned for optimization. Estimated minimum mean square deviation 

is marked in blue, observed minimum mean square deviation in blue. The learning rate is 0.27325. 

After the training stage, the model was subjected to fitting to the data of 2001-2022 for further 

processing. It is also worth noting that for the adjustment and forecasting process, those stations that 

operated between 2001 and 2022, at least for several years, were selected. They were then filled with 

CHIRPS satellite data (Palavandishvili, 2021). 

To perform the prediction, it was necessary to calculate a linear regression equation for each station 

using the polyfit function in MatLab space. After calculating the coefficients, polynomials were 

calculated in the MatLab space with the polyval function. 

y=ax+b 

From the parameters included in this equation, the following were selected: a - the standardized 

precipitation index calculated by the model, x - the value of the sum of the monthly precipitation for a 

specific scenario, b - the standardized value of the actual precipitation. 

In the first case of the forecast scenario, the amount of precipitation was determined from 0 cm to 10 

cm because we are interested in the process of drought development in the case of a small amount of 

precipitation. 

Table 3. Standardized Precipitation Index (SPI), 0 cm to 10 cm inclusive precipitation forecast for some regions of Kakheti 

Precipitation 

forecast (sm) 

Dedoplistskaro Sagarejo Gurjaani Khvareli Lagodekhi Omalo Telai 

0 -0.270 -0.336 -0.008 - 0.002 -0.524 0.270 -0.568 

1 -0.021 -0.057 -0.097 -0.012 -0.269 0.429 -0.325 

2 0.227 0.220 -0.186 -0.026 -0.014 0.587 -0.083 

3 0.475 0.498 -0.275 -0.041 0.240 0.746 0.159 

4 0.724 0.776 -0.365 -0.054 0.495 0.905 0.402 

5 0.973 1.054 -0.454 -0.068 0.750 1.064 0.645 

6 1.222 1.333 -0.543 -0.082 1.005 1.222 0.888 

7 1.470 1.611 -0.633 -0.096 1.260 1.381 1.131 

8 1.719 1.889 -0.722 -0.110 1.514 1.540 1.374 

9 1.968 2.167 -0.811 -0.124 1.769 1.698 1.617 

10 2.217 2.445 -0.901 -0.138 2.024 1.857 1.860 

Conclusion 

The analysis showed that Gurjaan and Kvareli are more vulnerable to a small amount of precipitation, 

and less vulnerable regions were also identified: Dedoplistskaro, Sagarejo, and Lagodekhi. When 

analyzing these results, station altitude and humidity should be considered, as well as the fact that in 

the case of Artana and Melaani stations some satellite data were not found; that’s why the model could 

not cover the full Kakheti region. 

To increase the accuracy of such research, it is necessary to determine additional parameters—

temperature, humidity, soil moisture, and indices calculated from the satellite—their validation and 

adjustment for the machine learning algorithm. Also, the period of education and training should be 

determined separately; the appropriate method for forecasting will be selected, by means of which we 

will get the regression equation. 

Competing interests 

The authors declare that they have no competing interests. 

Authors’ contribution 

A. Palavandishvili conceived of the presented idea, performed the analytic calculations, and took 

the lead in writing the manuscript.  

ORCID iD 

Ana Palavandishvili https://orcid.org/0000-0002-7254-685X 

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