3066-280XISSN (Online)
Volume 5 , Issue 2 , PP: 92-109, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud Elshabrawy Mohamed 1 * , Abdelaziz Rabehi 2
Doi: https://doi.org/10.54216/MOR.050205
Rainfall detection and forecasting are complex tasks in hydrology due to the nonlinear and multi-scale nature of precipitation processes. Recent advances in artificial intelligence (AI), deep learning, and metaheuristic optimization have significantly improved predictive accuracy across diverse geographic and climatic conditions. Deep learning models, such as ConvLSTM and hybrid CNN–LSTM systems, excel in capturing spatial and temporal dependencies, especially when combined with optimization algorithms like Whale Optimization and Ant Colony Optimization. These techniques help fine-tune model parameters, reduce errors, and prevent premature convergence. The integration of Extended Reality (XR) technologies, including Augmented Reality (AR) and Virtual Reality (VR), with AI-driven rainfall forecasting offers new opportunities for immersive visualization in water resource management. XR technologies enable real-time, interactive simulations of rainfall predictions and water distribution, enhancing decision-making for water management and climate adaptation planning. Despite challenges such as data scarcity and computational demands, the convergence of AI, metaheuristics, and XR technologies holds great promise for building resilient, accurate, and interpretable systems for global water resource management and flood mitigation.
ConvLSTM , Metaheuristic Optimization , Ensemble Forecasting , Rainfall Prediction , Hydrological Modeling
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