BIBECHANA Vol. 21, No. 3, December 2024, 272-280 ISSN 2091-0762 (Print), 2382-5340 (Online) Journal homepage: http://nepjol.info/index.php/BIBECHANA Publisher:Dept. of Phys., Mahendra Morang A. M. Campus (Tribhuvan University)Biratnagar Evaluating and predicting losses from thunderbolt and fire events in Nepal: A regression analysis with moving averages approach Pitri Bhakta Adhikari*, Rachana Ghimere, Nabraj Khanal Tri-Chandra M. Campus, Saraswati Sadan, Ghantaghar, Kathmandu, Nepal ∗Corresponding author. Email: pbadhikari09@gmail.com Abstract Nepal is prone to natural disasters, including thunderbolts and fires,due to its geographical location. However, the lack of awareness and preparedness has exacerbated the impact of these disasters, as evidenced by the current serious pandemic situation. Urgent attention is required in terms of financial, social, and technological development to effectively manage thunderbolt and fire incidents. This study examines the frequency and severity of thunderbolt and fire incidents in Nepal over the past 48 years, revealing a concerning trend of increasing losses and incidents. Despite this alarming situation, the issue remains largely overlooked. It is imperative for relevant agencies to take decisive actions to mitigate the losses caused by these disasters. Implementing awareness programs and ensuring preparedness are crucial steps towards reducing the impact of disasters and safeguarding communities from their devastating consequences. Keywords Disaster; Thunderstorm, Fire, Forecasting method. Article information Manuscript received: February 24, 2024; Revised: July 20, 2024; Accepted: August 17, 2024 DOI https://doi.org/10.3126/bibechana.v21i3.63069 This work is licensed under the Creative Commons CC BY-NC License. https://creativecommons. org/licenses/by-nc/4.0/ 1 Introduction Nepal, encompassing an area of 147,141 square kilo- meters, comprises diverse regions such as the hilly region, terai region, and Himalayas. The increas- ing global warming and climate change have re- sulted in various significant challenges worldwide, including shortened monsoon periods, erratic rain- fall, and increased occurrences of natural disasters such as thunderbolts, fires, landslides, floods, and droughts. Among these disasters, thunderbolts and fires stand out as the most destructive in Nepal due to its geographical diversity, making the country a hotspot for natural calamities [1]. Different re- gions of Nepal face distinct types of disasters on a daily basis, leading to numerous casualties, miss- 272 http://nepjol.info/index.php/BIBECHANA pbadhikari09@gmail.com https://doi.org/10.3126/bibechana.v21i3.63069 https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 273 ing individuals, and widespread devastation. In the terai region, thunderbolts, droughts, and floods are predominant causes of destruction, while land- slides and thunderbolts pose significant threats in the hilly region. Additionally, avalanches in the Hi- malayas region have claimed the lives of many in- dividuals [2]. The prevalent natural disasters in Nepal include thunderbolts, fires, landslides, and floods, with a fo- cus on thunderbolts and fires in this study. Fires have ecological effects on vegetation patterns in ecosystems, with both positive and negative im- pacts [3]. Forest fires result in economic, social, and ecological damage, necessitating prioritized for- est fire management through awareness programs and the utilization of forest firefighting tools to mitigate their widespread socio-economic, environ- mental, and ecological impacts. Ignoring forest fire management could hinder Nepal's overall develop- ment [4]. The majority of fire incidents occur during the pre-monsoon season, with tropical lowland, ever- green forests, and broad-leaved forests being at the highest risk, especially in areas with high tree cover density, lower elevations, and lower slopes [5]. The total burned area has seen a 0.6% increase since 2001 AD [6]. Lightning phenomena involve electri- cal discharge within a cloud or between a cloud and the earth, while thunderbolts are characterized by a flash of light accompanied by thunder [7]. Most thunderbolt incidents occur in April and May, while fires typically occur in March [8]. Between 2011 and 2021 AD, no lightning incidents were recorded in November, with maximum occurrences observed during the pre-monsoon period. The number of people injured due to lightning is nearly three times the number of fatalities [9, 10]. Investing in disas- ter preparedness and awareness programs is crucial, as demonstrated by Aryal's findings that investing 1 NPR before a disaster is equivalent to saving 18 NPR after a disaster [11]. Hazard identification can also help to prevent the fire scenarios. Numerous studies have explored the variability and impacts of natural hazards, yet only a limited number have focused on analyzing historical trends and forecasting future scenarios. This research is based on the examination and pro- jection of losses attributed to disasters, recogniz- ing the varying degrees of destructiveness among different types. Within the context of Nepal, our focus lies on thunderbolt and fire, deemed particu- larly destructive. Through this research, we aim to analyze the overall trend of disaster-related losses and utilize generalized equations to forecast future losses based on these trends. Therefore, prioritizing development work and implementing preparedness and awareness programs are essential in reducing losses due to disasters. 2 Methodology In conducting this research, the necessary data was sourced from the Disaster Risk Reduction (DRR) portal of the Ministry of Home Affairs (MoHA). Data spanning from 1971 AD to 2022 AD, covering a period of 48 years, was collected. To analyze the data, a moving average of 5 years was calculated, and each period was labeled from 0 to 47. For in- stance, period zero (0) represents the average from 1971 AD to 1975 AD, period one (1) represents the average from 1972 AD to 1976 AD, and so forth. Graphs were plotted for various variables such as the average number of incidents, total deaths, af- fected families, and number of injured people over time. Using the time series data, a regression equa- tion in the form of Y = a + bX was determined, where Y represents the dependent variable, X repre- sents the independent variable (time), 'a' represents the intercept, and 'b' represents the slope of the re- gression line. These graphs were analyzed using MS Excel. The regression line, which provides insights into the effects on human lives in future years, was fit- ted based on the trend identified from the plotted data. Error analysis was conducted by comparing observed values (Y) with estimated values (Y') ob- tained from the regression equation. The deviation between these values was calculated and plotted in tables for further analysis. The regression equation, expressed as a straight line in the form Y = mX + C, was utilized for this analysis. The normal equations derived from this equation are as follows:∑ Y = m ∑ X + nC (1) and ∑ XY = m ∑ X2 + C ∑ X (2) Solving these equations yields the slope (m) and intercept (C) of the straight line. The formulas used to calculate the slope and intercept are as follows: m = n ∑ XY− ∑ X ∑ Y n ∑ X2 −m ∑ X and, C = ΣY−m ∑ X n . By utilizing these formulas, the slope and inter- cept of the straight line are determined, providing valuable insights for the analysis. In this research, estimated values refer to those derived from the regression equation, while ob- served values denote secondary data obtained from the specified data source. The estimated values are selected at intervals of five consecutive years rather than encompassing all years, aiming to analyze the data distribution. Subsequently, the disparity be- tween estimated values (Y) and observed values (Y1) is tabulated for each variable, with this dif- ference termed as the error “e” (defined as the vari- ance between the two data points, estimated and Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 274 observed). This shows that the estimated values are how closely align with the observed values. Overall, the data highlights the increasing fre- quency and severity of both disasters fire and thunderbolt-related incidents over the years, em- phasizing the importance of effective disaster man- agement and preparedness measures to mitigate their impact on communities. 3 Results and Discussion The data collected spans from 1971 AD to 2022 AD, with each period representing a five-year aver- age using the moving average method. In the table provided, each row corresponds to a five-year pe- riod, labeled from zero to 46, where zero represents the average from 1971 AD to 1975 AD, 1 represents the average from 1972 AD to 1976 AD, and so forth. The table includes various metrics: 'Ave. NOI' rep- resents the average number of incidents, 'Ave. TD' represents the average total number of deaths, 'Ave. AF' represents the average total number of affected family, 'Ave. INJ' represents the average total num- ber of injured people due to thunderbolt. A. Thunderbolt From Table 1, it is observed that, the average number of incidents shows a generally increasing trend, particularly after year 34, where there is a rapid increase. The maximum number of deaths is recorded between years 34 and 42, with an average value exceeding 100. The number of affected fami- lies exhibits variability, ranging from zero to 1027 in average. This variation could be attributed to a lack of historical information or incomplete recording of disaster-related data in earlier years due to limited communication between societies. The maximum average number of injured individuals is recorded in years 45 and 46, exceeding 300. There is a no- table increase in the number of injured people after year 32, indicating a significant rise in incidents re- sulting in injuries. The provided graph displays the total number of incidents attributed to thunderbolts, with the orig- inal data represented by the blue dotted line. The graph highlights a sharp increase in incidents in re- cent years. Additionally, a regression line has been fitted to the data, represented by the red dotted line. This regression line serves as a predictive tool, Table 1: Moving average data of thunderbolt. Year Ave. NOI Ave. TD Ave. AF Ave. INJ 0 5.8 5.6 44.6 11.4 1 6.6 5.8 44.6 11.0 2 8.4 7.2 44.6 11.4 3 8.6 10.0 20.6 15.4 4 6.8 8.6 0.0 8.4 5 6.6 8.2 0.0 5.8 6 5.4 7.0 0.0 5.6 7 3.4 5.6 0.0 5.2 8 4.0 3.8 0.0 9.0 9 5.4 5.0 0.0 9.0 10 4.8 4.6 0.0 8.6 11 4.6 4.2 0.0 8.6 12 5.2 4.4 0.0 9.2 13 4.8 3.4 0.8 1.8 14 4.0 4.2 0.8 4.6 15 5.4 6.6 3.0 9.8 16 9.4 12.0 3.0 14.8 17 11.4 15.2 3.0 16.0 18 20.0 24.2 2.2 25.0 19 31.0 32.6 33.8 36.8 20 32.2 32.8 31.6 33.0 21 34.4 34.0 109.0 37.4 22 45.2 45.0 789.2 59.2 23 39.4 38.2 789.2 54.6 24 32.0 32.8 757.6 49.4 25 35.8 34.2 757.6 56.8 26 39.4 40.4 680.2 63.4 27 31.4 32.2 0.0 48.0 28 38.6 42.2 324.0 64.2 29 41.4 41.2 324.2 69.8 30 40.2 44.0 324.2 73.4 31 39.8 39.4 324.2 85.2 32 46.4 42.4 325.0 109.4 33 51.2 41.6 21.4 119.8 34 64.0 53.2 153.0 141.6 35 75.8 61.2 172.6 163.4 36 94.2 73.8 528.4 195.8 37 112.6 86.6 738.4 224.4 38 121.6 100.2 1027.2 252.2 39 137.0 103.6 896.0 206.8 40 149.0 109.2 888.8 209.8 41 162.0 111.8 584.8 197.6 42 169.2 106.8 425.6 186.8 43 192.6 95.6 188.4 241.0 44 233.8 95.0 293.2 285.8 45 265.2 90.8 403.0 310.4 46 265.6 78.2 412.4 301.0 47 280.8 78.6 439.0 295.4 indicating the expected future trend of thunderbolt-related incidents. The regression line equation, expressing the relationship between the average number of incidents (Y) and the indepen- dent variable (X), is: Y = -47.32 + 4.70X. In Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 275 this equation, Y represents the dependent variable, which is the average number of incidents of thun- derbolts and X represents the independent variable, which could be time or another relevant factor. This regression equation allows for forecasting fu- ture trends in the number of thunderbolt-related incidents based on the provided data and the iden- tified regression line. Table 2: Estimated and observed data of number of incidents due to thunderbolt Year (X) Estimated Y = -47.32 + 4.70X Observed Y1 Error e = Y - Y1 e2 0 -47.32 5.8 -53.12 2821.734 5 -23.82 6.6 -30.42 925.3764 10 -0.32 4.8 -5.12 26.2144 15 23.18 5.4 17.78 316.1284 20 46.68 32.2 14.48 209.6704 25 70.18 35.8 34.38 1181.984 30 93.68 40.2 53.48 2860.11 35 117.18 75.8 41.38 1712.304 40 140.68 149.0 -8.32 69.2224 45 164.18 265.2 -101.02 10205.04 The figure 2 is the graph of total death of the thunderbolt. Here straight line is the predicted line of average total death. From figure 2 it is clear that the total death due to thunderbolt is increas- ing sharply. The peak number of total deaths are seen in the year 35 to 45 and the lowest number of deaths are in the years 0 to 15. This is the regres- sion line of average total death, Y= -13.21+2.30 X. Table 3: Estimated and observed data of number of total deaths due to thunderbolt Year (X) Estimated Y = -13.21 + 2.30X Observed Y1 Error e = Y - Y1 e2 0 -13.21 5.6 -18.81 353.8161 5 -1.71 8.2 -9.91 98.2081 10 9.79 4.6 5.19 26.9361 15 21.29 6.6 14.69 215.7961 20 32.79 32.8 -0.01 0.0001 25 44.29 34.2 10.09 101.8081 30 55.79 44.0 11.79 139.0041 35 67.29 61.2 6.09 37.0881 40 78.79 109.2 -30.41 924.7681 45 90.29 90.8 -0.51 0.2601 The figure 3 is the graph of affected family. Here the straight line represents the predicted graph of affected family. There is the irregularity in the data structure. In some years the number of affected family is zero and in some years the number of af- fected family is high around thousand. The maxi- mum number of affected family is seen in the years 20 to 25 and 35 to 40. The number of affected fam- ily is zero in the years 5 to 20 is zero. This could happen because in the past years there is no suffi- cient data available and there could be missing the data. This is the regression line of average injured people, Y= 11.74 + 0.13 X. The figure 4 is the graph of injured people the straight-line is the predicted line for the thunder- bolt. The graph of injured people is also in in- creasing order in which the graph is not that much increasing till the year 30. Then after the graph is increasing sharply after the year 30 and the peak number of injured peoples due to thunderbolt is in year 45. This is the regression equation for injured people by thunderbolt, Y= -57.58 + 6.32 X. Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 276 Table 4: Estimated and observed data of number of affected families due to thunderbolt Year Estimated Y = -57.55 + 13.89X Observed Y1 Error e = Y - Y1 e2 0 -57.55 44.6 -102.15 10434.62 5 11.9 0.0 11.9 141.61 10 81.35 0.0 81.35 6617.823 15 150.8 3.0 147.8 21844.84 20 220.25 31.6 188.65 35588.82 25 289.7 757.6 -467.9 218930.4 30 359.15 324.2 34.95 1221.503 35 428.6 172.6 256.0 65536.0 40 498.05 888.8 -390.75 152685.6 45 567.5 403.0 164.5 27060.25 Table 5: Estimated and observed data of number of injured people due to thunderbolt Year Estimated Y = -57.55 + 6.32X Observed Y1 Error e = Y - Y1 e2 0 -57.55 11.4 -68.95 4754.103 5 -25.95 5.8 -31.75 1008.063 10 5.65 8.6 -2.95 8.7025 15 37.25 9.8 27.45 753.5025 20 68.85 33.0 35.85 1285.223 25 100.45 56.8 43.65 1905.323 30 132.05 73.4 58.65 3439.823 35 163.65 163.4 0.25 0.0625 40 195.25 209.8 -14.55 211.7025 45 226.85 310.4 -83.55 6980.602 Figure 1: Graph of average number of incidents due to thunderbolt. Figure 2: Graph of average number of total death due to thunderbolt. Figure 3: Number of affected family due to thun- derbolt. Figure 4: Number of injured people due to thun- derbolt. Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 277 B. Fire Table 6 presents data on fire incidents, includ- ing moving averages for the number of incidents, total deaths, affected families, and injured people across 47 different years. In this table, the peri- ods are labeled from 0 to 46, where 0 represents the moving average from 1971 AD to 1975 AD, and subsequent periods represent consecutive five-year averages. 'Ave. NOI' represents the average num- ber of incidents, 'Ave. TD' represents the average number of total deaths, 'Ave. AF' represents the average number of affected families, 'Ave. INJ' rep- resents the average number of injured people. From the Table 2, it is observed that the number of in- cidents shows an increasing trend as the number of year approaches 47, with the maximum recorded at year 47, averaging 2542.8 incidents. Year 47 corre- sponds to the average from 2018 AD to 2022 AD and the total number of deaths also exhibits an in- creasing trend, reaching its peak at year 47 with an average of 84.8 deaths. The number of affected fam- ilies follows a similar pattern, increasing steadily until year 25, then reaching a peak value exceed- ing 10,000, before decreasing thereafter. Similarly, the number of injured people shows a consistent in- crease over the years, with the maximum average of 345 injuries recorded at year 47. Overall, the data indicates a concerning trend of increasing fire incidents, resulting in higher num- bers of deaths, affected families, and injured indi- viduals, especially in recent years. These observa- tions underscore the importance of effective fire pre- vention and management strategies to mitigate the impact of fires on communities. Fig. 5 is the graph of average number of inci- dents of fire from 1971 to 2022 AD. And the straight line is the predicted line which helps to predicts pos- sible number of incidents in future. The number of incidents due to the fire is sharply increasing after the year 40. Till the year 40 the graph is flat. This is the regression equation for number of incidents. By the help of this equation the future forecasting is possible by this the future events can be predicted. The regression line equation is Y= - 315.77 + 29.48 X. Fig. 6 is the graph of average number of total deaths. And the straight line is the predicted line for future events. From the figure 6 the graph is increasing sharply and there is certain increase in the year 20 to 25. And then it increasing from year 35. Here the straight line which gives the predic- tion that how many peoples would die in the latest years due to fire. The regression equation for fire which helps to predict the average total deaths in future is Y = 9.76 + 1.25 X. Table 6: Moving average data of fire Year ave. NOI ave. TD ave. AF ave. INJ 0 54.2 10.0 6859.8 5.4 1 78.4 17.0 8475.4 12.8 2 87.4 17.8 8607.0 22.8 3 87.2 16.8 8740.4 23.4 4 91.4 24.6 8972.6 22.8 5 91.8 25.0 3771.0 32.4 6 77.2 19.4 2407.4 34.8 7 71.0 19.6 2458.8 25.4 8 66.2 18.8 2893.0 27.4 9 66.4 13.0 3395.6 27.2 10 66.4 13.2 3230.4 19.2 11 61.8 14.4 3665.2 9.6 12 63.2 13.6 5214.4 9.6 13 62.8 14.2 4834.0 6.0 14 76.8 14.8 4661.8 7.8 15 78.8 13.8 4176.6 7.8 16 89.0 15.0 3803.4 10.6 17 123.6 22.6 3544.0 10.4 18 142.0 25.6 4886.0 15.0 19 136.4 30.8 6001.0 18.8 20 153.8 49.0 10089.6 26.0 21 160.4 67.8 17355.6 37.8 22 154.4 74.4 19457.4 46.8 23 134.6 73.4 17928.2 45.0 24 133.0 70.8 16486.2 41.0 25 132.8 58.8 12753.6 34.8 26 157.8 45.8 5462.0 28.6 27 134.8 33.0 2258.2 20.0 28 155.2 36.4 3995.4 20.4 29 155.6 32.6 3009.0 22.0 30 148.2 27.6 2780.2 21.6 31 122.6 25.2 2492.2 22.2 32 129.2 26.4 2091.4 25.8 33 160.6 32.8 3129.8 51.0 34 211.4 41.6 5595.8 73.4 35 259.6 50.6 6791.6 82.2 36 299.8 49.4 9192.8 93.6 37 361.0 58.0 15127.8 119.8 38 362.6 53.8 14049.8 108.4 39 474.8 54.0 11594.4 101.4 40 526.6 56.8 10164.4 103.0 41 766.2 62.6 8455.2 131.2 42 978.6 62.6 2968.6 142.4 43 1407.8 70.0 1991.0 192.2 44 1788.2 72.6 2707.6 241.6 45 2086.6 69.0 3181.6 290.8 46 2321.0 76.2 3174.4 314.4 47 2542.8 84.8 3300.4 345.0 The figure 7 is the graph of average number of total affected family by fire from 1971 to 2022 AD. And the straight line is the predicted line which helps to predicts future events. The peak num- ber of affected peoples is seen in year 20 to 25. Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 278 And the overall graph is seen constant and has an average value of around 6500. The regression equation for average number of affected family is, Y=6502.22+5.39 X. Fig. 8 is the graph of total number of injured people and the straight line is the predicted line which helps to predict future events. The graph 8 shows that the number of injured peoples due to fire is also increasing and has a peak value at year 47. The graph is sharply increased after the year 30. There is no significant increase in the trend till the year 30. The regression equation for injured people by fire is, Y = -37.53 + 4.37 X. While the literature is rich in studies on haz- ard management, there is a notable absence of re- search focusing specifically on the trend analysis and prediction of losses over time. This manuscript uniquely emphasizes the examination of past loss trends to forecast future losses. Our observation indicates a significant upward trajectory in losses compared to previous years, especially in recent times. Overall, our study aims to analyze the es- calating trend in losses, particularly notable in re- cent years. However, it's important to acknowledge that the prediction of future losses may encounter challenges due to potential variations in the data obtained from the sources, which could introduce additional errors into the forecasting process. Table 7: Estimated and observed data of number of incidents due to fire Year Estimated Y = -315.77 + 29.48X Observed Y1 Error e = Y-Y1 e2 0 -315.77 54.2 -369.97 136877.8 5 -168.37 91.8 -260.17 67688.43 10 -20.97 66.4 -87.37 7633.517 15 126.43 78.8 47.63 2268.617 20 273.83 153.8 120.03 14407.2 25 421.23 132.8 288.43 83191.86 30 568.63 148.2 420.43 176761.4 35 716.03 259.6 456.43 208328.3 40 863.43 526.6 336.83 113454.4 45 1010.83 2086.6 -1075.77 1157281 Table 8: Estimated and observed data of number of total deaths due to fire Year Estimated Y = 9.76 + 1.2X Observed Y1 Error e = Y - Y1 e2 0 9.76 10 -0.24 0.0576 5 15.76 25 -9.24 85.3776 10 21.76 13.2 8.56 73.2736 15 27.76 13.8 13.96 194.8816 20 33.76 49 -15.24 232.2576 25 39.76 58.8 -19.04 362.5216 30 45.76 27.6 18.16 329.7856 35 51.76 50.6 1.16 1.3456 40 57.76 56.8 0.96 0.9216 45 63.76 69 -5.24 27.4576 Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 279 Table 9: Estimated and observed data of number of affected families due to fire Year Estimated Y = 6502.22 + 5.38X Observed Y1 Error e = Y - Y1 e2 0 6502.22 6859.8 -357.58 127863.5 5 6529.12 3771 2758.12 7607226 10 6556.02 3230.4 3325.62 11059748 15 6582.92 4176.6 2406.32 5790376 20 6609.82 10089.6 -3479.78 12108869 25 6636.72 12753.6 -6116.88 37416221 30 6663.62 2780.2 3883.42 15080951 35 6690.52 6791.6 -101.08 10217.17 40 6717.42 10164.4 -3446.98 11881671 45 6744.32 3181.6 3562.72 12692974 Table 10: Estimated and observed data of number of injured people due to fire Year Estimated Y = -37.53 + 4.37X Observed Y1 Error e = Y - Y1 e2 0 -37.53 5.4 -42.93 1842.985 5 -15.68 32.4 -48.08 2311.686 10 6.17 19.2 -13.03 169.7809 15 28.02 7.8 20.22 408.8484 20 49.87 26 23.87 569.7769 25 71.72 34.8 36.92 1363.086 30 93.57 21.6 71.97 5179.681 35 115.42 82.2 33.22 1103.568 40 137.27 103 34.27 1174.433 45 159.12 290.8 -131.68 17339.62 Figure 5: Graph of number of incidents due to fire. Figure 6: Graph of number of total death due to fire. Pitri Bhakta Adhikari et al./ BIBECHANA 21 (2024) 272-280 280 Figure 7: Graph of number of affected family due to fire. Figure 8: Graph of number of injured people due to fire. 4 Conclusion The data clearly indicates a significant increase in losses and incidents, particularly in fire and thun- derbolt incidents. The period from 2018 AD to 2022 AD stands out as having peak averages of 2542.8 fire incidents and 280.8 thunderbolt inci- dents. Graphs depicting these parameters show a sharp upward trend, highlighting the pressing need for intervention. Given the continuous rise in in- cidents and losses, it's evident that without ma- jor actions, there is no sign of reduction in these disasters. Therefore, it is imperative for the rele- vant agencies and the government to devise a clear plan to mitigate the losses caused by these disas- ters. Effective disaster management strategies, in- cluding well-preparedness measures and awareness programs, are crucial in reducing both human and material losses significantly. By enhancing pre- paredness and raising awareness about these dis- asters, communities can better protect themselves and their assets, ultimately reducing the overall im- pact of these calamities. Acknowledgment We acknowledge to UGC for the Faculty Research Grant with the Grant number: FRG- 79/80 – S & T – 10 for providing the research grant. Also, giv- ing thanks to the Tri-Chandra Multiple Campus, Tribhuvan University, for generously providing the necessary research facilities. The authors also want to thank DRR portal to give the free access of the data and analysis, essential for the successful com- pletion of our research. References [1] Nepal Government. Nepal Parichaya. Kath- mandu: Nepal Government, 2022. [2] T. D. Ricardo and K. Elisabeth. 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Rupan- taran: A Multidisciplinary Journal, 2020. [9] P. B. Adhikari. People deaths and injuries caused by lightning in himalayan region, nepal. International Journal of Geophysics, June 2022. [10] P. B. Adhikari. Different measurement sys- tem of lightning. European Journal of Applied Physics, 5(3):26–31, June 2023. [11] P. Aryal. Occurrence of disaster events and their impact in nepal: Role of government and civil society organizations to reduce the disas- ter risks. Nepal Journal of Multidisciplinary Research, pages 88–101, April 2023. Introduction Methodology Results and Discussion Conclusion