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Article

Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin

by 1,2,3,4, 1,2,4,*, 1,2,4, 1, 1, 2,4, 3 and 2,4,5
1
Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China
2
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
3
Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
4
Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
5
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Academic Editors: Zheng Duan and Babak Mohammadi
Water 2022, 14(11), 1692; https://doi.org/10.3390/w14111692
Received: 24 April 2022 / Revised: 18 May 2022 / Accepted: 23 May 2022 / Published: 25 May 2022
Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales. View Full-Text
Keywords: mid- to long-term runoff prediction; deep learning models; time lag; lead time mid- to long-term runoff prediction; deep learning models; time lag; lead time
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MDPI and ACS Style

Ren, Y.; Zeng, S.; Liu, J.; Tang, Z.; Hua, X.; Li, Z.; Song, J.; Xia, J. Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin. Water 2022, 14, 1692. https://doi.org/10.3390/w14111692

AMA Style

Ren Y, Zeng S, Liu J, Tang Z, Hua X, Li Z, Song J, Xia J. Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin. Water. 2022; 14(11):1692. https://doi.org/10.3390/w14111692

Chicago/Turabian Style

Ren, Yuanxin, Sidong Zeng, Jianwei Liu, Zhengyang Tang, Xiaojun Hua, Zhenghao Li, Jinxi Song, and Jun Xia. 2022. "Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin" Water 14, no. 11: 1692. https://doi.org/10.3390/w14111692

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