Monthly streamflow time series are highly non-linear. How to improve forecast accuracy is a
great challenge in hydrological studies. A lot of research has been conducted to address the …

This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-
runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural …

Modeling and forecasting the flow of rivers, especially in flood-prone areas using warning
systems, enables officials to take the required measures for cutting the damage. On the other …

Reliable hourly flood forecasting using weather radar rainfall data for early warning system
is essential for reducing natural disaster risk during extreme typhoon events. This study …

Abstract Model performance evaluation for real-time flood forecasting has been conducted
using various criteria. Although the coefficient of efficiency (CE) is most widely used, we …

Providing reliable reservoir water level forecasts is a challenge because of the accumulative
errors in hydrological and reservoir routing models. We present a novel forecasting model …

Flood forecasting has been recognized as one of the most important and reliable ways for
flood management. It is therefore necessary to improve the reliability and accuracy of the …

Real-time flood forecasting is important for decision-making with regards to flood control and
disaster reduction. The conventional approach involves a postprocessor calibration strategy …

The streamflow estimation in ungauged or poorly gauged basins is a fundamental and
challenging problem in hydrology, which has often been solved by transferring hydrological …

Accurate real-time flood forecasting is essential for flood control and warning system,
reservoir operation and other relevant water resources management activities. The objective …