Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still
room for improvement, researchers have been developing physical and machine learning …

The machine learning algorithm 'random forest'has been applied in many areas of water
resources research including discharge simulation. Due to low setup and operation cost …

Rainfall–runoff modeling has been of great importance for flood control and water resource
management. However, the selection of hydrological models is challenging to obtain …

Runoff prediction has a pivotal role in the flood warning system. For mountainous small-
sized watersheds, establishing a reliable and efficient model to forecast flood is multifarious …

High-elevation mountain regions, central to global freshwater supply, are experiencing more
rapid warming than low-elevation locations. High-elevation streams are therefore potentially …

There is growing empirical evidence that many river basins across the US Midwest have
been experiencing an increase in the frequency of flood events over the most recent …

In the hydrology field, hydrological forecasting is regarded as one of the most challenging
engineering tasks, as runoff has significant spatial–temporal variability under the influences …

Efficient and accurate streamflow predictions are important for urban water management.
Data-driven models, especially neural network (NN) models can predict streamflow fast …

Accurate streamflow prediction is of great significance for water resource management. In
recent years, data-driven models such as artificial neural networks (ANNs) and support …

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic
processes to ensure safe, sufficient, and equal water distribution. These hydrologic data …