干旱区生态环境知识资源中心(Arid): A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China

Arid

DOI	10.2166/nh.2024.124
	A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China
	Fang, J. J.; Yang, Linshan; Wen, Xiaohu; Li, Weide; Yu, Haijiao; Zhou, Ting
通讯作者	Fang, JJ
来源期刊	HYDROLOGY RESEARCH
ISSN	1998-9563
EISSN	2224-7955
出版年	2024
卷号	55 期号:2 页码:180-204
英文摘要	Accurate streamflow prediction is crucial for effective water resource management. However, reliable prediction remains a considerable challenge because of the highly complex, non-stationary, and non-linear processes that contribute to streamflow at various spatial and temporal scales. In this study, we utilized a convolutional neural network (CNN)-Transformer-Long short-term memory (LSTM) (CTL) model for streamflow prediction, which replaced the embedding layer with a CNN layer to extract partial hidden features, and added a LSTM layer to extract correlations on a temporal scale. The CTL model incorporated Transformer's ability to extract global information, CNN's ability to extract hidden features, and LSTM's ability to capture temporal correlations. To validate its effectiveness, we applied it for streamflow prediction in the Shule River basin in northwest China across 1-, 3-, and 6-month horizons and compared its performance with Transformer, CNN, LSTM, CNN-Transformer, and Transformer-LSTM. The results demonstrated that CTL outperformed all other models in terms of predictive accuracy with Nash-Sutcliffe coefficient (NSE) values of 0.964, 0.912, and 0.856 for 1-, 3-, 6-month ahead prediction. The best results among the five comparative models were 0.908, 0.824, and 0.778, respectively. This indicated that CTL is an outstanding alternative technique for streamflow prediction where surface data are limited.
英文关键词	convolutional neural network (CNN) long short-term memory (LSTM) Shule River streamflow prediction Transformer
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001139663900001
WOS关键词	TRANSFORMER ; RUNOFF ; RAINFALL
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404131
推荐引用方式 GB/T 7714	Fang, J. J.,Yang, Linshan,Wen, Xiaohu,et al. A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China[J],2024,55(2):180-204.
APA	Fang, J. J.,Yang, Linshan,Wen, Xiaohu,Li, Weide,Yu, Haijiao,&Zhou, Ting.(2024).A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China.HYDROLOGY RESEARCH,55(2),180-204.
MLA	Fang, J. J.,et al."A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China".HYDROLOGY RESEARCH 55.2(2024):180-204.