A hydrological model is a useful tool to study the effects of human activities and climate
change on hydrology. Accordingly, the performance of hydrological modeling is vitally …

Flood forecasting is an essential non-engineering measure for flood prevention and disaster
reduction. Many models have been developed to study the complex and highly random …

Incomplete representations of physical processes often lead to structural errors in process-
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …

Streamflow forecasting is essential for hydrological engineering. In accordance with the
advancement of computer aids in this field, various machine learning (ML) models have …

This study examines whether deep learning models can produce reliable future projections
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …

Despite the considerable success of deep learning methods in modelling physical
processes, they suffer from a variety of issues such as overfitting and lack of interpretability …

Study region Flood formation involves complex nonlinear processes and numerous
variables, with data-driven models becoming a key non-engineering approach to flood …

Over the past 10 years, urban floods have increased in frequency because of extreme
rainfall events and urbanization development. To reduce the losses caused by floods …

Making accurate and reliable probability density forecasts of flood processes is
fundamentally challenging for machine learning techniques, especially when prediction …

Rapid and accurate urban flood forecasting with high temporal resolution is critical to
address future flood risks under urbanization and climate change. Machine learning models …