Analyzing Machine Learning and Deep Learning Models for Enhanced Hailstorm Detection and Forecasting

Amongst all types of damaging weather, hailstorms rank among the worst and have significant negative impacts on the environment as well as the economy. Therefore, researchers are now applying ML and DL to improve hailstorm identification and prediction to help tackle this problem. This research looks into several models, such as Decision Trees (DTs), Back Propagation Neural Networks (BPNNs), Random Forest (RF) classifiers, Deep Learning (DL), Neural Networks (NNs), and Support Vector Machines (SVMs). Every model has its own advantages. As an example, CNNs are very good at finding complicated spatial patterns, RBFNs are very good at handling nonlinear situations quickly, and hybrid approaches get better accuracy by combining different methods. However, problems such as region-specific loci, heavy computing requirements, and relatively small datasets hinder performance. More complex models like CNN-LSTM and DAM-EfficientNet prove promising, the latter more remarkable for its impressive accuracies and robustness. These developments have not eradicated the problems of having datasets that are inconsistent and the sample numbers being inadequate. This research highlights the advantages of integrating ML and DL approaches to create models that are reliable, scalable, and precise. These new developments could potentially revolutionize hailstorm detection, enhance weather forecasts, and provide better crisis management strategies for real-time applications by overcoming the limitations that exist currently.