River water level usually given by nonlinear and nonstationary time series and affected by
numerous complex spatial and temporal factors. But not all input factors are positively …

In order to reduce the loss caused by flood, a large number of researches based on data,
algorithms, machine learning and other technical means are used to realize flood …

The small-and medium-sized watersheds have complex and varied hydrogeological
features, boundary conditions, and human activities. There are nonlinear interactions …

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta,
crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding …

Runoff prediction is of great significance to flood defense. However, due to the complexity
and randomness of the runoff process, it is hard to predict daily runoff accurately, especially …

With the rapid development of IoT, big data and artificial intelligence, the research and
application of data-driven hydrological models are increasing. However, when conducting …

It is still very challenging to enhance the accuracy and stability of daily runoff forecasts,
especially several days ahead, owing to the non-linearity of the forecasted processes. Here …

With advances in artificial intelligence, machine learning-based models such as long short-
term memory (LSTM) models have shown much promise in forecasting long-term runoff by …

Runoff forecasting is important for water resource management. Although deep learning
models have substantially improved the accuracy of runoff prediction, the temporal and …

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 …