Abstract
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
Runoff forecasting is of considerable significance to water resources management
The objective of this study is to explore the ability and stability of Temporal Convolutional Network (TCN) and the integration of TCN with EncoderDecoder architecture (TCN-ED) for runoff forecasting with multi-step ahead times
In order to compare the performance of TCN and TCN-ED in the learning and forecasting phase, the minimum, mean, and maximum Nash–Sutcliffe efficiency (NSE) and volumetric efficiency (VE) values averaging over 24
Summary
Runoff forecasting is of considerable significance to water resources management. Accurate runoff forecasting can guide the hydraulic engineering construction, reservoir. According to the extent of physical principles, models for runoff forecasting can be divided into two categories: process-driven models and data-driven models (Yuan et al ). Process-driven models represent a specific physical process employing experimental formulas before inputting data. Lin et al | TCN combined with Encoder-Decoder framework for runoff forecasting
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