Prediction of Yangtze River streamflow based on deep learning neural network with El Nino-Southern Oscillation.

ABSTRACT: Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced

SUBMITTER: Ha S 

PROVIDER: S-EPMC8175427 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature