With the escalating severity of global water shortage problems, the management and efficient utilization of water resources have become a critical research focus [1,2]. In China, the total volume of freshwater resources amounts to 2800 billion cubic meters, representing 6% of global water resources and ranking fourth globally, following Brazil, Russia, and Canada [3]. However, China’s per capita water resources stand at only 2300 cubic meters, equivalent to just a quarter of the global average, categorizing it among the countries with the scarcest per capita water resources globally [4]. In China, precipitation diminishes from the southeast coast to the northwest inland, categorized into five zones: rainy, humid, semi-humid, semi-arid, and arid [5]. Owing to the heterogeneous distribution of precipitation across regions, a pronounced supply–demand imbalance of water resources exists in China, particularly in the northern regions [6]. With population growth and economic advancement, the demand for water resources steadily increases, while the supply remains limited, leading to water shortages in some areas [7]. In some regions, water resource utilization efficiency in China is noticeably low, primarily due to inadequate focus on water resources, insufficient effective water-saving measures, and a lack of institutional guarantees [8]. Furthermore, with the accelerated pace of industrialization and urbanization, substantial quantities of industrial and agricultural wastewater, domestic sewage, and solid waste are persistently discharged, leading to severe pollution of rivers, lakes, and groundwater [9]. Consequently, there is a critical need to enhance the protection and utilization of water resources and to implement effective measures aimed at improving water resource utilization efficiency [10].

Water resources constitute renewable resources and demonstrate variability in annual and interannual quantities, possessing a distinct cycle and pattern (Figure 1). Influenced by solar radiation, Earth’s gravity, and other physical processes, including evapotranspiration, precipitation, soil infiltration, surface runoff, and underground flow, water translocates from one location to another [11]. Water resources form a critical foundation for the survival and development of human society [12]. To enhance the management and utilization of water resources, various computational models have been employed in water resource management [13]. The traditional hydrological model operates on the principle of the hydrological cycle, articulating the components of the hydrological cycle system [14]. These models hold significant physical importance and are straightforward to elucidate; however, their development process is complex and requires extensive expertise from developers [15]. Machine learning can process massive amounts of data, extract valuable information, and automatically construct models to predict future trends [16]. In water resource management, such data enable a more comprehensive consideration of problems and the establishment of improved decision-making models [17]. Consequently, machine learning, owing to its robust data mining capabilities, is widely applied in areas like water resource supply and demand prediction, flood risk management, water quality monitoring, and forecasting scenarios [18,19,20,21,22].