干旱区生态环境知识资源中心(Arid): Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China

Arid

DOI	10.1080/02626667.2024.2374868
	Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China
	Yu, Haijiao; Yang, Linshan; Feng, Qi; Barzegar, Rahim; Adamowski, Jan F.; Wen, Xiaohu
通讯作者	Yang, LS
来源期刊	HYDROLOGICAL SCIENCES JOURNAL
ISSN	0262-6667
EISSN	2150-3435
出版年	2024
卷号	69 期号:11 页码:1501-1522
英文摘要	Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine learning and deep learning models. Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe efficiency values of 0.976, 0.967, and 0.957, BMA achieved the greatest accuracy for 1-, 2-, and 3-day-ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of BMA in yielding consistently accurate streamflow forecasts. Thus, BMA could provide an efficient alternative approach to multistep-ahead daily streamflow forecasting. The incorporation of data decomposition techniques (e.g. variational mode decomposition) and deep learning algorithms (e.g. deep belief network) into BMA may provide worthy technical references for supervised learning of streamflow systems in data-scarce regions.
英文关键词	Bayesian model averaging ensemble learning decomposition oasis streamflow forecasting
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001282793700001
WOS关键词	WATER-RESOURCES ; ARID REGION ; REGRESSION ; ALGORITHM ; FLOW ; ACCURACY ; NETWORKS ; SYSTEM ; OASIS ; BASIN
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/2XILL650/404124
推荐引用方式 GB/T 7714	Yu, Haijiao,Yang, Linshan,Feng, Qi,et al. Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China[J],2024,69(11):1501-1522.
APA	Yu, Haijiao,Yang, Linshan,Feng, Qi,Barzegar, Rahim,Adamowski, Jan F.,&Wen, Xiaohu.(2024).Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China.HYDROLOGICAL SCIENCES JOURNAL,69(11),1501-1522.
MLA	Yu, Haijiao,et al."Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China".HYDROLOGICAL SCIENCES JOURNAL 69.11(2024):1501-1522.