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This study presents a deep learning-based approach using Long Short-Term Memory (LSTM) networks for short-term thunderstorm nowcasting in Nosy Be, Madagascar, addressing the limitations of traditional forecasting methods. The developed models achieve high accuracies of 90.25% for 1-hour and 89.32% for 3-hour predictions by leveraging real-time satellite-derived meteorological data. The findings highlight the effectiveness of LSTM networks in capturing temporal and spatial dynamics of storms, ultimately enha

Author

bruno

Language

EN