Deep Belief Networks and LSTM for Precise Weather Forecasting

Heavy rainfall often leads to various natural hazards, making accurate rainfall forecasting crucial for implementing effective preventive measures. This study proposes a novel three-phase forecasting model comprising Pre-processing, Feature Extraction, and Forecasting to improve prediction accuracy. The model leverages advanced deep learning algorithms, specifically Deep-Belief Networks (DBN) and Long Short-Term Memory (LSTM), to analyze historical rainfall data and detect complex patterns. The primary objective of this work is to enhance forecasting precision by identifying intricate relationships within meteorological data, thereby providing valuable insights for disaster preparedness and risk mitigation. The proposed model’s performance is systematically evaluated against existing approaches using multiple metrics, demonstrating its superior predictive capabilities. By enabling early interventions, this method contributes to proactive disaster management, reducing the impact of extreme weather events.