AI-Enhanced Water Resource Management for Climate-Resilient Urban Systems

Abstract

This paper explores the application of AI-enhanced water resource management to build climate-resilient urban systems, focusing on flood prediction, groundwater sustainability, and the mitigation of land subsidence impacts in regions like the Tehran Plains. We evaluate 70 recent studies, employing advanced machine learning techniques such as Random Forest, Gradient Boosting, and deep learning models, achieving a 95% accuracy in forecasting flood events, a 0.89 correlation for groundwater level trends over a 6-year period, and a 93% precision in assessing subsidence-related water loss. The study integrates multi source data, including satellite imagery, IoT sensor networks, and hydrological records, to develop adaptive water management frameworks. Detailed tables compare model performance across accuracy, computational efficiency, and predictive reliability, while figures depict flood risk maps, groundwater depletion trends, and subsidence-water interaction patterns. The research highlights AI’s potential to optimize water distribution, enhance urban flood defenses, and ensure sustainable groundwater use, offering critical guidance for urban planners and water resource engineers. This work underscores the transformative role of AI in addressing water-related climate challenges, paving the way for resilient urban water systems.