干旱区生态环境知识资源中心(Arid): The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin

Arid

DOI	10.1007/s11356-023-26961-y
	The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin
	Wang, Zitao; Wang, Jianping; Yu, Dongmei; Chen, Kai
通讯作者	Wang, JP
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2023
卷号	30 期号:23 页码:63991-64005
英文摘要	Groundwater is a vital resource in arid areas that sustains local industrial development and environmental preservation. Mapping groundwater potential zones and determining high-potential regions are essential for the responsible use of the local groundwater resource. When utilizing machine learning or deep learning algorithms to forecast groundwater potential in arid areas, difficulties such as inaccurate and overfitting predictions might occur due to a shortage of borehole samples. In this study, a database of groundwater conditioning factors with a size of 275,157 x 9 was created in the Qaidam Basin, and 85 known borehole samples were collected. The groundwater potential was evaluated using a combination of rank sum ratio (RSR), projection pursuit regression (PPR) and random forest (RF) algorithms, resulting in four models: PPR, RSR-PPR, RSR-RF, and RF. Results indicated that the groundwater potential was higher in mountainous regions surrounding the Qaidam Basin and decreased progressively towards the central and northwestern regions where most industries and facilities are located. The two primary factors, according to the PPR and RF models, were evapotranspiration (0.246, 0.225) and landform (0.176, 0.294). In terms of their ability to accurately forecast the borehole samples, the four models ranked as follows: RF > RSR-RF > RSR-PPR > PPR. The accuracy of the four models in the low-potential area was 0.73 (PPR), 0.60 (RSR-PPR), 0.87 (RSR-RF), and 0.80 (RF), respectively. However, the RF model showed overfitting due to a lack of samples, especially in high-potential regions, which limits its applicability. The RSR-RF method was applied directly to evaluate the entire factor database, avoiding the risk of overfitting caused by a limited number of training samples. The results demonstrate that the RSR-RF model is effective for classifying groundwater potential types in samples and mapping groundwater potential of the study area. This research presents a novel approach for groundwater potential predictions in areas with insufficient sample sizes, providing a reference for policymakers and researchers.
英文关键词	Groundwater potential Qaidam Basin Rank sum ratio (RSR) Projection pursuit regression (PPR) Random forest (RF) Overfitting
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000968993200001
WOS关键词	RANDOM FOREST ; GIS ; RISK ; MULTIVARIATE ; PREDICTION ; DYNAMICS ; DISTRICT ; PROVINCE ; MODELS ; AREA
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396268
推荐引用方式 GB/T 7714	Wang, Zitao,Wang, Jianping,Yu, Dongmei,et al. The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin[J],2023,30(23):63991-64005.
APA	Wang, Zitao,Wang, Jianping,Yu, Dongmei,&Chen, Kai.(2023).The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(23),63991-64005.
MLA	Wang, Zitao,et al."The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.23(2023):63991-64005.