干旱区生态环境知识资源中心(Arid): A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions

Arid

DOI	10.1016/j.ecolind.2023.110092
	A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions
	Chu, Haibo; Wu, Jin; Wu, Wenyan; Wei, Jiahua
通讯作者	Wu, J
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2023
卷号	148
英文摘要	Daily streamflow forecasting is a major determinant of ecological processes in running waters, healthy stream ecology and surrounding environment, and accurate streamflow forecasting provides a powerful foundation for ecological assessment, management, and decision-making. Recently, data-driven models for different flow re-gimes have shown excellent potential in streamflow forecasting. However, the boundaries between different flow regimes were selected arbitrarily without considering the changes in boundaries that often occur over time in the real world. Therefore, in this paper, an integrated modelling approach that couples a dynamic classification method with a long short-term memory networks (LSTM) model without data transformation (the DC-LSTM model) and an LSTM with Box-Cox data transformation (the DC-B-LSTM model) is developed to improve the performance of streamflow forecasting considering different flow regimes. The boundaries of dynamic classifi-cation are dynamic changing interval values of related hydrological variables improved from traditional clas-sification method just using static single-variable threshold, so dynamic classification can more fully explore the relationship and information of hydrological data. The performance of both the DC-LSTM and DC-B-LSTM models is compared to that of the LSTM model without data classification (the traditional LSTM model) and with data classification using a traditional static method (the C-LSTM model) based on data from 8 stations within 4 river basins in different climate regions in the United States. The results show that both the DC-LSTM and DC-B-LSTM models out-perform the traditional LSTM models (with or without static data classification) for all river basins considered. Furthermore, the DC-B-LSTM model displays better performance than the DC-LSTM model in arid areas.
英文关键词	Dynamic classification Long short-term memory networks Streamflow forecasting Box-Cox method
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:000955834900001
WOS关键词	INDEX
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395921
推荐引用方式 GB/T 7714	Chu, Haibo,Wu, Jin,Wu, Wenyan,et al. A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions[J],2023,148.
APA	Chu, Haibo,Wu, Jin,Wu, Wenyan,&Wei, Jiahua.(2023).A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions.ECOLOGICAL INDICATORS,148.
MLA	Chu, Haibo,et al."A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions".ECOLOGICAL INDICATORS 148(2023).