AI-Driven Hydrological Modeling Advanced Water Discharge Estimation for Single Gauging Stations in River Basins

AI-driven hydrological modeling has transformed water resource management, particularly in estimating water discharge at single gauging stations within river basins.This study harnesses the power of Convolutional Neural Networks (CNNs) to overcome common challenges in traditional hydrological methods, which often fail to capture non-linear relationships and face issues with sparse in-situ measurements.By incorporating satellite-retrieved data and high-resolution imagery, our proposed system markedly improves the accuracy of predictions for streamflow, discharge, and water quality.Experimental data from various river basins showcase the effectiveness of CNNs in enhancing hydrological forecasts, providing dependable discharge estimates even in ungauged and high-mountain regions.This research highlights the potential of AI techniques in managing urban runoff and predicting suspended sediment loads.Future work should aim at refining AI models, exploring hybrid approaches, and further integrating advanced satellite and remote sensing technologies to advance hydrological forecasting and water resource management strategies.