Storm Whisperers: Predicting Thunderstorms with Long Short-Term Heuristic Memory Model

Kalyan Chatterjee, Bhoomeshwar Bala, Raja Shekar Kadurka, Katla Aruna Jyothi, Harish Kanakalla, Mada Prasad, Ruifeng Hu, Saurav Mallik 

Accurate thunderstorm prediction is essential for safeguarding public safety and optimizing resource management, particularly in increasingly unpredictable weather patterns. This study explores the use of Long Short-Term Memory (LSTM) neural networks enhanced with heuristic mechanisms as a cutting-edge method for predicting thunderstorm events. By leveraging the inherent ability of LSTMs to capture long-range temporal dependencies, the proposed heuristic-based LSTM (LSTHM) model systematically analyzes historical meteorological data to discern critical patterns indicative of thunderstorm formation. The LSTHM framework enhances the model’s robustness in diverse climatic conditions through its heuristic mechanisms. The network is trained using a comprehensive dataset, encompassing varied weather scenarios to ensure generalizability and accuracy. Performance evaluation against traditional forecasting methodologies reveals that the LSTHM model consistently demonstrates superior predictive accuracy and reliability in estimating the onset and intensity of thunderstorms. The results substantiate the efficacy of the proposed approach, highlighting its potential to improve forecasting precision and elucidate the complex dynamics underlying storm development. This research significantly contributes to meteorological prediction, showcasing the applicability of machine learning and deep learning techniques in advancing weather forecasting models. Ultimately, the insights derived from this study aim to enhance timely decision-making processes during weather-related emergencies, thereby mitigating the impacts of severe thunderstorms on vulnerable communities.