Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting

Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting

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 network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long sh...

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Bibliographic Details
Main Authors: Ye Tian, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, Qian Zhu
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Water
Subjects:
recurrent neural networks
long-short term memory
hydrological modeling
uncertainty
stream flow forecasting
Online Access:https://www.mdpi.com/2073-4441/10/11/1655

Internet

https://www.mdpi.com/2073-4441/10/11/1655

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