RfGANNNet 2.0: A Hybrid AI Framework Integrating Random Forests, Spatio-Temporal Graph Convolution, and Physics-Guided GANs for High-Resolution Rainfall Forecasting

Accurate rainfall forecasting underpins effective water resource management, disaster mitigation, and agricultural planning. This paper proposes RfGANNNet 2.0, a hybrid AI framework that combines Random Forests, Spatio-Temporal Graph Convolutional Networks (ST-GCN), and Physics-Guided Generative Adversarial Networks (GANs) to deliver high-resolution and generalizable rainfall predictions. We present a comprehensive review of AI-driven techniques from 2018 to 2025, including ensemble models such as AdaNAS, deep learning architectures like ConvLSTM and Temporal Fusion Transformer (TFT), and spatio-temporal attention mechanisms. The model integrates satellite and radar remote sensing data, addresses limitations related to data sparsity, and incorporates Explainable AI methods (e.g., SHAP, LIME) to interpret model outputs. Extensive evaluations using benchmark datasets and metrics such as RMSE, MAE, and AUC highlight the robustness and accuracy of the proposed approach. Future research directions include real-time edge computing, adaptive transfer learning, and advanced data fusion to improve operational readiness and performance in extreme weather scenarios.