Hydrological Model

Hydrological models aim to simulate the movement and storage of water within catchments, supporting water resource management and flood forecasting. Current research emphasizes improving model efficiency and accuracy, particularly in data-scarce regions, through the integration of machine learning (ML) techniques, including deep learning, recurrent neural networks, and graph convolutional networks, alongside traditional physics-based models. This hybrid approach leverages the strengths of both paradigms—physical process understanding and data-driven prediction—to enhance model extrapolability and reduce computational demands. The resulting improvements in prediction accuracy and efficiency have significant implications for water resource management and disaster preparedness globally.