Integrating artificial intelligence and machine learning in hydrological modeling for sustainable resource management

Recent advancements in hydrological modeling, including the integration of Artificial Intelligence (AI) and Machine Learning (ML), have revolutionized our ability to provide hydrological insights with greater precision. Widely used hydrological models in recent years include SWAT, SWAT+, HEC-HMS, MIKE SHE, MODFLOW, DHSVM, VIC, WEAP, and HYDRUS. Our comparative analysis highlights key findings (1) SWAT/SWAT+ demonstrated superior performance in agricultural regions; (2) HEC-HMS excelled in flood forecasting, with peak flow prediction errors as low as 5%; (3) MIKE SHE proved most effective for integrated surface-groundwater modeling in complex watersheds; and (4) MODFLOW exhibited the highest accuracy in groundwater simulations. Additionally, (5) SWAT/SWAT+ are optimal for agricultural management and water quality assessment, with extensive use in Best Management Practices; (6) HEC-HMS is most suitable for real-time flood forecasting, particularly in small to medium watersheds; and (7) MIKE SHE/MODFLOW are preferred for comprehensive groundwater-surface water interaction studies. Furthermore, (8) DHSVM and VIC showed strengths in mountainous and large-scale watershed modeling; (9) WEAP proved most effective for policy-integrated water resource planning; and (10) HYDRUS demonstrated the highest accuracy in simulating soil-water-plant interactions at field scales. These models enhance informed decision making and effective watershed management globally, helping to develop sustainable solutions amid growing environmental pressures. [GRAPHICS]