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

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. 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.;

Keywords

• artificial neural network
• deep learning
• encoder-decoder architecture
• runoff forecasting
• temporal convolutional network