干旱区生态环境知识资源中心(Arid): A comparison of machine learning models for the mapping of groundwater spring potential

Arid

DOI	10.1007/s12665-020-08944-1
	A comparison of machine learning models for the mapping of groundwater spring potential
	Al-Fugara, A'; kif 1; Pourghasemi, Hamid Reza 2; Al-Shabeeb, Abdel Rahman 3; Habib, Maan 4; Al-Adamat, Rida 3; AI-Amoush, Hani 5; Collins, Adrian L.6
通讯作者	Pourghasemi, Hamid Reza
来源期刊	ENVIRONMENTAL EARTH SCIENCES
ISSN	1866-6280
EISSN	1866-6299
出版年	2020
卷号	79 期号:10
英文摘要	Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping. Accordingly, this research compared the predictive powers of five machine learning models for groundwater potential spatial mapping in Wadi az-Zarqa watershed in Jordan. The five models were random forest (RF), boosted regression tree (BRT), support vector machine (SVM), mixture discriminant analysis (MDA), and multivariate adaptive regression spline (MARS). These algorithms explored spatial distributions of 12 hydrological-geological-physiographical (HGP) conditioning factors (slope, altitude, profile curvature, plan curvature, slope aspect, slope length (SL), lithology, soil texture, average annual rainfall, topographic wetness index (TWI), distance to drainage network, and distance to faults) that determine where groundwater springs are located. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the prediction accuracies of the five individual models. Here the results were ranked in descending order as MDA (83.2%), RF (80.6%), SVM (80.2%), BRT (78.0%), and MARS (75.5%).The results show good potential for further use of machine learning techniques for mapping groundwater spring potential in other places where the use and management of groundwater resources is essential for sustaining rural or urban life.
英文关键词	Machine learning models Groundwater mapping Geographic information system Variable importance Jordan
类型	Article
语种	英语
国家	Jordan ; Iran ; England
收录类别	SCI-E
WOS记录号	WOS:000533395400001
WOS关键词	SUPPORT VECTOR MACHINE ; RANDOM FOREST ; LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; FREQUENCY RATIO ; NEURAL-NETWORKS ; GIS TECHNIQUES ; WEST-BENGAL ; RECHARGE ; VULNERABILITY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319075
作者单位	1.Al Al Bayt Univ, Fac Engn, Dept Surveying Engn, Mafraq 25113, Jordan; 2.Shiraz Univ, Coll Agr, Dept Nat Resources & Environm Engn, Shiraz, Iran; 3.Al Al Bayt Univ, Inst Earth & Environm Sci, Dept GIS & Remote Sensing, Mafraq 25113, Jordan; 4.Al Balqa Appl Univ, Dept Surveying & Geomat Engn, Al Salt 19117, Jordan; 5.Al Al Bayt Univ, Inst Earth & Environm Sci, Dept Earth Sci & Environm, Mafraq 25113, Jordan; 6.Rothamsted Res, Sustainable Agr Sci, Okehampton EX20 2SB, Devon, England
推荐引用方式 GB/T 7714	Al-Fugara, A',kif,Pourghasemi, Hamid Reza,et al. A comparison of machine learning models for the mapping of groundwater spring potential[J],2020,79(10).
APA	Al-Fugara, A'.,kif.,Pourghasemi, Hamid Reza.,Al-Shabeeb, Abdel Rahman.,Habib, Maan.,...&Collins, Adrian L..(2020).A comparison of machine learning models for the mapping of groundwater spring potential.ENVIRONMENTAL EARTH SCIENCES,79(10).
MLA	Al-Fugara, A',et al."A comparison of machine learning models for the mapping of groundwater spring potential".ENVIRONMENTAL EARTH SCIENCES 79.10(2020).