干旱区生态环境知识资源中心(Arid): Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest

Arid

DOI	10.1016/j.chemosphere.2021.133388
	Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest
	He, Song; Wu, Jianhua; Wang, Dan; He, Xiaodong
通讯作者	Wu, JH (corresponding author)，Changan Univ, Sch Water & Environm, 126 Yanta Rd, Xian 710054, Shaanxi, Peoples R China.
来源期刊	CHEMOSPHERE
ISSN	0045-6535
EISSN	1879-1298
出版年	2022
卷号	290
英文摘要	Groundwater quality in plains and basins of arid and semi-arid regions with increased agriculture and urbanization development faces severe nitrate pollution, which is affected by both climate and anthropogenic activities. Here, shallow groundwater nitrate concentrations in the Yinchuan Region in central Yinchuan Plain were modeled during 2000, 2005, 2010, and 2015 using random forest. Multiple spatial environment factors were taken as predictor variables. The relative importance of these factors was also calculated using the constructed model. Remote sensing and GIS methods were used to compile various environmental factors to generate training and test sets for training and validation of the random forest model. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R-2) between the observed and predicted groundwater nitrate concentrations were used to measure the model performance. As indicated by these metrics, the random forest model for groundwater nitrate prediction was performed well. The relative importance of the predictor variables computed by the model indicated groundwater nitrate was mainly affected by the distance to the Yellow River, meteorological elements (precipitation, evaporation, and mean air temperature), and water level elevation. Additionally, urban and arable land were the two land use/land cover types that mainly influenced groundwater nitrate concentration in the Yinchuan Region, of which urban land was more influential than arable land as a result of intense expansion of urban land from 2000 to 2015. Overall, the current study provides an approach to integrate multiple environmental factors for groundwater quality study and is also significant for sustainable groundwater management in the Yinchuan Region.
英文关键词	Groundwater nitrate Environmental factors Machine learning Random forest Predictor variable importance
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000755593400003
WOS关键词	HEALTH-RISK ASSESSMENT ; URBAN LAND-USE ; YINCHUAN PLAIN ; SURFACE-WATER ; QUALITY ; CONTAMINATION ; SHALLOW ; REGION ; BASIN ; 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/2XILL650/376229
作者单位	[He, Song; Wu, Jianhua; Wang, Dan; He, Xiaodong] Changan Univ, Sch Water & Environm, 126 Yanta Rd, Xian 710054, Shaanxi, Peoples R China; [He, Song; Wu, Jianhua; Wang, Dan; He, Xiaodong] Changan Univ, Key Lab Subsurface Hydrol & Ecol Effects Arid Reg, Minist Educ, 126 Yanta Rd, Xian 710054, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	He, Song,Wu, Jianhua,Wang, Dan,et al. Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest[J],2022,290.
APA	He, Song,Wu, Jianhua,Wang, Dan,&He, Xiaodong.(2022).Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest.CHEMOSPHERE,290.
MLA	He, Song,et al."Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest".CHEMOSPHERE 290(2022).