Explicit Monitoring and Prediction of Hailstorms With XGBoost Classifier for Sustainability

Abstract

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.