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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| Published in: | Hydrology Research Vol. 51; no. 5; pp. 1136 - 1149 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2020
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| ISSN: | 0029-1277, 1998-9563, 2224-7955 |
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| Abstract | 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. |
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| AbstractList | 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. HIGHLIGHTS For the first time, TCN and TCN-ED models are proposed to forecast runoff.; TCN-ED has better performance than TCN in runoff forecast in this study.; The concentration time is a critical threshold to the effective forecast horizon.; Both models perform better in median and high flow than in low flow.; It is subject to the forecast horizon for both models to forecast peak flow.; 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. |
| Author | Xu, Chong-Yu Guo, Shenglian Lin, Kangling Zhou, Yanlai Sheng, Sheng Li, Zhiyu Liu, Feng Chen, Hua Chen, Jie |
| Author_xml | – sequence: 1 givenname: Kangling surname: Lin fullname: Lin, Kangling organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China and Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China – sequence: 2 givenname: Sheng surname: Sheng fullname: Sheng, Sheng organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China and Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China – sequence: 3 givenname: Yanlai surname: Zhou fullname: Zhou, Yanlai organization: Department of Geosciences, University of Oslo, P O Box 1047, Blindern, N-0316, Oslo, Norway – sequence: 4 givenname: Feng surname: Liu fullname: Liu, Feng organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 5 givenname: Zhiyu surname: Li fullname: Li, Zhiyu organization: Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA – sequence: 6 givenname: Hua surname: Chen fullname: Chen, Hua organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China and Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China, Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA – sequence: 7 givenname: Chong-Yu surname: Xu fullname: Xu, Chong-Yu organization: Department of Geosciences, University of Oslo, P O Box 1047, Blindern, N-0316, Oslo, Norway – sequence: 8 givenname: Jie surname: Chen fullname: Chen, Jie organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China and Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China – sequence: 9 givenname: Shenglian surname: Guo fullname: Guo, Shenglian organization: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China and Hubei Provincial Key Lab of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China |
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| SubjectTerms | 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 |
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