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

文献类型：期刊论文

作者Ren, Yuanxin1,2,3,4; Zeng, Sidong1,2,4; Liu, Jianwei1,2,4; Tang, Zhengyang1; Hua, Xiaojun1; Li, Zhenghao2,4; Song, Jinxi3; Xia, Jun2,4,5
刊名WATER
出版日期2022-06-01
卷号14期号:11页码:21
关键词mid- to long-term runoff prediction deep learning models time lag lead time
DOI10.3390/w14111692
通讯作者Zeng, Sidong(zengsidong@cigit.ac.cn)
英文摘要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.
资助项目Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science Foundation[ZH20020001] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040500] ; Youth Innovation Promotion Association, CAS[2021385] ; Central Guidance on Local Science and Technology Development Fund of Chongqing City[2021000069]
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:000808653400001
出版者MDPI
源URL[http://119.78.100.138/handle/2HOD01W0/16314]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Zeng, Sidong
作者单位1.China Yangtze Power Co Ltd, Hubei Key Lab Intelligent Yangtze & Hydroelect Sc, Yichang 443000, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
3.Northwest Univ, Coll Urban & Environm Sci, Shaanxi Key Lab Earth Surface Syst & Environm Car, Xian 710127, Peoples R China
4.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China
5.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Ren, Yuanxin,Zeng, Sidong,Liu, Jianwei,et al. Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin[J]. WATER,2022,14(11):21.
APA Ren, Yuanxin.,Zeng, Sidong.,Liu, Jianwei.,Tang, Zhengyang.,Hua, Xiaojun.,...&Xia, Jun.(2022).Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin.WATER,14(11),21.
MLA Ren, Yuanxin,et al."Mid- to Long-Term Runoff Prediction Based on Deep Learning at Different Time Scales in the Upper Yangtze River Basin".WATER 14.11(2022):21.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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