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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Vydané v:Hydrology Research Ročník 51; číslo 5; s. 1136 - 1149
Hlavní autori: Lin, Kangling, Sheng, Sheng, Zhou, Yanlai, Liu, Feng, Li, Zhiyu, Chen, Hua, Xu, Chong-Yu, Chen, Jie, Guo, Shenglian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London IWA Publishing 01.10.2020
Predmet:
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
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Shrnutí: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 impact on the forecast ability, and the concentration time of the basin is a critical threshold to the effective forecast horizon for both models. Both models perform poorly in the low flow and well in the medium and high flow at most forecast horizons, while it is subject to the forecast horizon in forecasting peak flow. TCN-ED has better performance than TCN in runoff forecasting, with higher accuracy, better stability, and insensitivity to fluctuations in the rainfall process. Therefore, TCN-ED is an effective deep learning solution in runoff forecasting within an appropriate forecast horizon.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0029-1277
1998-9563
2224-7955
DOI:10.2166/nh.2020.100