干旱区生态环境知识资源中心(Arid): Using machine learning algorithms to map the groundwater recharge potential zones

Arid

DOI	10.1016/j.jenvman.2020.110525
	Using machine learning algorithms to map the groundwater recharge potential zones
	Pourghasemi, Hamid Reza; Sadhasivam, Nitheshnirmal; Yousefi, Saleh; Tavangar, Shahla; Nazarlou, Hamid Ghaffari; Santosh, M.
通讯作者	Pourghasemi, HR
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2020
卷号	265
英文摘要	Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model; DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.
英文关键词	Groundwater recharge Machine learning algorithms Variable importance LASSO
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000533526800046
WOS关键词	SUPPORT VECTOR MACHINE ; SPATIAL PREDICTION ; FREQUENCY RATIO ; RANDOM-FOREST ; GENETIC ALGORITHM ; AQUIFER RECHARGE ; DECISION-MAKING ; LAND SUBSIDENCE ; SURFACE-WATER ; TREE MODELS
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/2XILL650/324489
作者单位	[Pourghasemi, Hamid Reza] Shiraz Univ, Coll Agr, Dept Nat Resources & Environm Engn, Shiraz, Iran; [Sadhasivam, Nitheshnirmal] Bharathidasan Univ, Sch Earth Sci, Dept Geog, Tiruchirappalli 620024, Tamil Nadu, India; [Yousefi, Saleh] AREEO, Soil Conservat & Watershed Management Res Dept, Chaharmahal & Bakhtiari Agr & Nat Resources Res &, Shahrekord, Iran; [Tavangar, Shahla] Tarbiat Modare Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn, Tehran, Iran; [Nazarlou, Hamid Ghaffari] Howard Univ, Dept Civil & Environm Engn, Washington, DC 20059 USA; [Santosh, M.] China Univ Geosci Beijing, Sch Earth Sci & Resources, Beijing 100083, Peoples R China; [Santosh, M.] Univ Adelaide, Dept Earth Sci, Adelaide, SA 5005, Australia
推荐引用方式 GB/T 7714	Pourghasemi, Hamid Reza,Sadhasivam, Nitheshnirmal,Yousefi, Saleh,et al. Using machine learning algorithms to map the groundwater recharge potential zones[J],2020,265.
APA	Pourghasemi, Hamid Reza,Sadhasivam, Nitheshnirmal,Yousefi, Saleh,Tavangar, Shahla,Nazarlou, Hamid Ghaffari,&Santosh, M..(2020).Using machine learning algorithms to map the groundwater recharge potential zones.JOURNAL OF ENVIRONMENTAL MANAGEMENT,265.
MLA	Pourghasemi, Hamid Reza,et al."Using machine learning algorithms to map the groundwater recharge potential zones".JOURNAL OF ENVIRONMENTAL MANAGEMENT 265(2020).