Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …

Landslides triggered by extreme rainfall due to global climate change are becoming more
frequent. The Earth surface processes activity and landform evolution caused by landslide …

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

There exists a substantial disparity in the distribution of streamflow gauge and basin
characteristic information, with a majority of flood observations being recorded from a limited …

Prediction of river flow rates is an essential task for both flood protection and optimal water
resource management. The high uncertainty associated with basin characteristics …

Streamflow forecasting is crucial in water planning and management. Physically-based
hydrological models have been used for a long time in these fields, but improving forecast …

With the development of intelligent manufacturing and automation, the precision and
complexity of mechanical equipment are increasing, which leads to a higher requirement for …

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological
uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid …

In recent years, applications of convolutional neural networks (CNNs) to runoff prediction
have received some attention due to their excellent feature extraction capabilities. However …