Nature-Based Solutions Optimization Using AI and Machine Learning for Sustainable Urban Water Management

The literature review demonstrates that Nature-Based Solutions (NBS) and Artificial Intelligence (AI)/Machine Learning (ML) are emerging as transformative approaches for sustainable urban water management. NBS provide multifunctional  benefits such as flood mitigation, groundwater recharge, pollutant removal, carbon sequestration, and enhancement of urban biodiversity. Meanwhile, AI and ML techniques have significantly improved hydrological forecasting, water quality prediction, infrastructure monitoring, and system optimization. Advanced algorithms including deep learning, ensemble models, and metaheuristic optimization methods have shown strong capability in handling complex, nonlinear urban water systems.   Despite these advancements, existing studies often address NBS implementation, predictive modeling, and optimization techniques separately rather than as an integrated framework. There is limited research that combines real-time monitoring, GIS-based spatial suitability analysis, multi-objective optimization, and adaptive AI models specifically for enhancing NBS performance. Furthermore, challenges such as data availability, lack of standardized performance metrics, computational complexity, scalability issues, and the need for transparent and explainable AI systems remain barriers to large- scale practical implementation.   Future research should focus on developing a comprehensive AI– ML-driven decision-support framework that integrates hydrological modeling, spatial analysis, predictive analytics, and optimization for Nature-Based Solutions. Incorporating climate change projections and scenario-based simulations will improve long-term resilience planning. Real-time IoT-based monitoring systems should be integrated with adaptive learning algorithms to enable dynamic system control. Additionally, emphasis should be placed on multi-objective optimization that balances hydrological efficiency, ecological benefits, and economic feasibility. Expanding pilot implementations across diverse urban contexts and enhancing stakeholder engagement will further  strengthen the applicability and scalability of smart, data-driven, and climate-resilient urban water management systems

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