干旱区生态环境知识资源中心(Arid): Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities

Arid

DOI	10.1016/j.jhydrol.2023.130404
	Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities
	Zhou, Yuanyuan; Tang, Qiuhong; Zhao, Gang
通讯作者	Tang, QH
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	627
英文摘要	Machine learning algorithm has been increasingly used to fill missing daily streamflow data from neighboring gauges in data-scarce regions. However, how human activities, especially reservoir construction, may affect the performance of these gap-filling algorithms has not been explicitly assessed. This study applied the MissForest algorithm (MF) to infill daily streamflow gaps at 58 selected stations in the north area of Tianshan Mountains, an arid region in northwest China. The stations without reservoir impacts (i.e., with natural flow regimes) and with reservoir impacts (i.e., with high impacts of human activities) have been separately used to implement the infilling such that the impact of human activities on the performance of MF can be analyzed. Results show that MF using the station without reservoir impacts performed very well in filling daily streamflow gaps. Its performance had been insignificantly influenced by an increasing amount of missing data, different numbers of stations, and different lengths of observed data at each station. Without human activities, MF performance obviously improved with an increasing of 2.2%-3.2% in mean of R2 and a reducing of 21%-40% in mean of SMAPE. However, the performance of MF exhibited a noticeable degradation as a result of human activities that mean of R2 reduced by 1.1%-3.4% and mean of SMAPE increased by -19.5%-30.6%. This could be attributed to the fact that the MF algorithm is unable to fully capture the relevant information regarding human activities. The reconstructed daily streamflow series showed little flow regime change at the stations without reservoir impacts, but an advanced peak flow from July to June and a decrease of peak flow by 13% at the stations with reservoir impacts during the period of 2006-2011 compared with the baseline period of 1964-1989. It is recommended to first fill in the data gaps at stations without human activity, and then fill in the data gaps at stations influenced by human activities. We recommend exercising caution when using the MF method in rivers where human activities are significant or where low flow and intermittent flow events occur frequently.
英文关键词	MissForest algorithm (MF) Gaps infilling Daily streamflow Tianshan Mountains Human activities
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001110909700001
WOS关键词	RANDOM FOREST ; IMPUTATION ; MODELS ; RIVER ; REGION ; CHINA
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397433
推荐引用方式 GB/T 7714	Zhou, Yuanyuan,Tang, Qiuhong,Zhao, Gang. Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities[J],2023,627.
APA	Zhou, Yuanyuan,Tang, Qiuhong,&Zhao, Gang.(2023).Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities.JOURNAL OF HYDROLOGY,627.
MLA	Zhou, Yuanyuan,et al."Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities".JOURNAL OF HYDROLOGY 627(2023).