The exploration of a Temporal Convolutional Network combined with Encoder-Decoder framework for runoff forecasting

The Temporal Convolutional Network (TCN) and TCN combined with the Encoder-Decoder architecture (TCN-ED) are proposed to forecast runoff in this study. Both models are trained and tested using the hourly data in the Jianxi basin, China. The results indicate that the forecast horizon has a great impa...

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Veröffentlicht in:	Hydrology Research Jg. 51; H. 5; S. 1136 - 1149
Hauptverfasser:	Lin, Kangling, Sheng, Sheng, Zhou, Yanlai, Liu, Feng, Li, Zhiyu, Chen, Hua, Xu, Chong-Yu, Chen, Jie, Guo, Shenglian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London IWA Publishing 01.10.2020
Schlagworte:	artificial neural network Coders Concentration time deep learning encoder-decoder architecture Encoders-Decoders Forecasting High flow Horizon Hydrology Low flow Mathematical models Neural networks Precipitation Rain Rainfall Runoff Runoff forecasting Stability Stream flow Support vector machines temporal convolutional network
ISSN:	0029-1277, 1998-9563, 2224-7955
Online-Zugang:	Volltext
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