Hailstorms are extremely dangerous for both people and property, hence precise forecasting techniques are required. To increase hailstorm forecast accuracy, this study suggests utilizing the XGBoost algorithm. The gradient boosting technique XGBoost is well-known for its effectiveness at managing intricate datasets and nonlinear relationships. The suggested approach improves prediction abilities by incorporating many meteorological factors and historical hailstorm data. The model outperforms conventional approaches through thorough evaluation utilizing cross-validation techniques. XGBoost, or extreme gradient boosting, is an excellent technique for hailstorm prediction because of its scalability, robustness, and proficiency with complicated datasets. By using the XGBoost algorithm, there is a chance to increase the accuracy of hailstorm predictions and decrease the socio-economic effects of these occurrences. To increase forecasting accuracy and mitigation tactics, this work demonstrates advances in hailstorm prediction using numerical weather models and machine learning approaches.
TopIntroduction
Large, destructive hailstones that occur during thunderstorms are what define hailstorms as natural phenomena. These icy missiles, which can range in size from tiny pellets to balls the size of a golf ball, can seriously damage infrastructure, towns, and agriculture. Since it enables prompt warnings and preparedness, predicting hailstorms has become an essential component of meteorological research, thereby reducing the possibility of loss and damage (C. Kishor Kumar Reddy,2023).
It is crucial to comprehend the intricate dynamics that result in hail formation to create precise prediction models. Severe thunderstorms are usually the setting for hailstorms, as powerful updrafts transport raindrops into the cold upper atmosphere. The first hailstone is formed when these supercooled water droplets come into contact with ice nuclei and freeze. These hailstones keep piling up as they are carried by the storm's updrafts up and down (Bhushan, B., 2023).
Artificial intelligence and machine learning have become essential parts of hailstorm prediction models in recent years. These tools find patterns and trends related to hail episodes by analyzing large datasets that include historical weather patterns, atmospheric conditions, and storm features. Over time, machine learning systems can increase the accuracy of hail forecasts by iteratively improving their predictions based on fresh data. Our capacity to forecast and lessen the effects of hailstorms has significantly improved with the incorporation of this cutting-edge technology into meteorological procedures (P. R. Anisha,2023).
Hailstorm prediction has come a long way, yet problems still exist. Because atmospheric processes are dynamic and contain a large number of variables, it is challenging to anticipate particular events with absolute precision. Research endeavors persist in honing prediction models, integrating novel data sources, and augmenting our comprehension. To address the global impact of hailstorms, international coordination is essential. Research results, technology developments, and shared data all contribute to a group effort to increase prediction accuracy and create practical mitigation plans. Ongoing study is even more important in addressing the shifting problems posed by a changing climate, as it influences the frequency and intensity of severe weather occurrences, including hailstorms (Williams, J.K., 2017).
Hailstorm prediction is a diverse field that depends on cutting-edge technologies, ongoing research, and meteorological knowledge. Reducing the negative effects of these natural events on the economy and society requires precise hailstorm forecasting. In the beginning, an effort was made to identify the vulnerable areas, the times that hailstorms occurred, and their frequency, both within and between the four homogeneous regions of India the North, Central & West, East and Northeast, and South. The statistics for the frequency of hailstorms in the various regions of the nation are displayed in Figure 1, Figure 2, and Figure 3 (Wang, P., 2018).
Northern region: The frequency distribution and number of hailstorm days are displayed in Fig, which indicates that throughout the last 35 years, hailstorms have occurred in the Northern region in Himachal Pradesh for 20 years, with May 1994's 13 days and March 1986's 8 days having the highest frequency (Kumar, S., 2023). Additionally, during the study period, hailstorms occurred more frequently in Himachal Pradesh on a single day, with the highest frequency occurring in May. In Punjab hailstorms occurred in Uttar Pradesh and Haryana for 20 years, in Jammu & Kashmir for 10 years, and in other places for 22 years.. The longest hailstorm in Punjab happened in March 1986, lasting eight days, while the longest hailstorm in Rajasthan occurred in April 1991, lasting five days (McWilliams, 2019).
With a maximum of eight days in March 1986, Uttarakhand (4 years) and Delhi (5 years) had the fewest hailstorms during the research period. It should be mentioned that in March 1986, there were significant hailstorms in the North (McWilliams, 2019).