Exploring Deep Learning and Machine Learning Models for HailStorm Detection

Abstract

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Hailstone detection is vital for weather forecasting and disaster management, helping to predict and mitigate damage caused by hailstorms. As machine learning (ML) and deep learning (DL) techniques continue to advance, there is significant potential to improve the precision and efficiency of hailstone detection systems. This study offers a comprehensive analysis of various ML and DL models for hailstone detection, aiming to enhance the accuracy and efficiency of real-time hailstorm prediction. The research evaluates models such as Convolutional Neural Networks (CNN), Random Forest (RF), and hybrid DL architectures, including Long Short-Term Memory-3D Convolutional Networks (LSTM-C3D), based on performance parameters like accuracy, precision, etc., Probability of Detection (POD), False Alarm Rate (FAR), and Critical Success Index (CSI). Consequences indicate that while CNNs excel in detecting "No-Hail" instances, they struggle with detecting "Hail," showing low precision and recall for hail detection. In contrast, RF models demonstrate a more balanced performance across both categories, achieving high precision and recall for "No-Hail" and "Hail." The LSTM-C3D hybrid model outperformed all others, showing superior accuracy and F1-score by capturing both spatial and temporal features crucial for detecting hailstones. Despite these promising results, challenges such as class imbalance and computational efficiency in real-time applications remain. The study highlights the potential of hybrid deep learning models and suggests future directions for improving hailstone detection systems, including the incorporation of additional meteorological data, class imbalance mitigation strategies, and real-time optimization.