Artificial Intelligence, Optimization, and Modeling Techniques in Water Resources Management: Challenges and Future Directions

The novelty of this work lies in its comprehensive investigation of six major elements of water resources management (watershed models, surrogate models, optimization techniques, Artificial Intelligence (AI), Decision Support Systems (DSS), and monitoring systems) in a single review. While previous studies have focused on individual elements or limited interactions, this paper offers a holistic perspective, addressing the risks of fragmented approaches that yield incomplete solutions. Unlike technical reviews, it blends depth with practical insights, aiding policymakers and managers while outlining gaps and new directions. Several recent successes have the potential to revolutionize water resources management. For example, integrating AI with traditional watershed models has significantly improved model performance. Additionally, AI-driven flood risk prediction has achieved impressive accuracy, enhancing the reliability of early warning systems. Meanwhile, future research should enhance water resource management by integrating AI, remote sensing, and the Internet of Things (IoT) to improve accuracy, efficiency, and adaptability.