Hydrological models represent water movement in natural systems, and they are important for water resource planning and management. But the models depend on reliable input data for weather factors, ...

Artificial intelligence has transformed hydrological modelling by offering robust tools for capturing complex and nonlinear processes that govern the movement and distribution of water. Data-driven ...

Machine learning has revolutionised hydrological modelling by offering data-driven alternatives to traditional process-based approaches. Algorithms such as deep neural networks and ensemble learning ...