干旱区生态环境知识资源中心(Arid): Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Arid

DOI	10.3390/w13162273
	Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models
	Namous, Mustapha; Hssaisoune, Mohammed; Pradhan, Biswajeet; Lee, Chang-Wook; Alamri, Abdullah; Elaloui, Abdenbi; Edahbi, Mohamed; Krimissa, Samira; Eloudi, Hasna; Ouayah, Mustapha; Elhimer, Hicham; Tagma, Tarik
通讯作者	Pradhan, B (corresponding author), Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Sydney, NSW 2007, Australia. ; Pradhan, B (corresponding author), Univ Kebangsaan Malaysia, Inst Climate Change, Earth Observat Ctr, Bangi 43600, Selangor, Malaysia. ; Lee, CW (corresponding author), Kangwon Natl Univ, Div Sci Educ, Chuncheon Si 24341, Gangwon Do, South Korea.
来源期刊	WATER
EISSN	2073-4441
出版年	2021
卷号	13 期号:16
英文摘要	The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.
英文关键词	drinking and irrigation water scarcity groundwater potential mapping machine learning remote sensing GIS karstic mountainous aquifers Morocco
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000690233900001
WOS关键词	ER-RABIA BASIN ; LOGISTIC-REGRESSION ; RECHARGE ; GIS ; SCALE ; WATER ; RISK ; CLIMATE ; AQUIFER
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
来源机构	King Saud University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364836
作者单位	[Namous, Mustapha; Krimissa, Samira; Ouayah, Mustapha] Sultan Moulay Slimane Univ, Polydisciplinary Fac, Lab Biotechnol & Sustainable Dev Nat Resources, Mghila BP 592, Beni Mellal 23000, Morocco; [Hssaisoune, Mohammed; Eloudi, Hasna] Ibn Zohr Univ, Fac Sci, Appl Geol & Geoenvironm Lab, Agadir 80000, Morocco; [Hssaisoune, Mohammed] Ibn Zohr Univ, Fac Sci Appl, BO 6146, Ait Melloul 86153, Morocco; [Pradhan, Biswajeet] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Sydney, NSW 2007, Australia; [Pradhan, Biswajeet] Univ Kebangsaan Malaysia, Inst Climate Change, Earth Observat Ctr, Bangi 43600, Selangor, Malaysia; [Lee, Chang-Wook] Kangwon Natl Univ, Div Sci Educ, Chuncheon Si 24341, Gangwon Do, South Korea; [Alamri, Abdullah] King Saud Univ, Coll Sci, Dept Geol & Geophys, POB 2455, Riyadh 11451, Saudi Arabia; [Elaloui, Abdenbi] Sultan Moulay Slimane Univ, Fac Sci & Tech, Water & Remote Sensing Team GEVARET, Beni Mellal 23000, Morocco; [Edahbi, Mohamed] Sul...
推荐引用方式 GB/T 7714	Namous, Mustapha,Hssaisoune, Mohammed,Pradhan, Biswajeet,et al. Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models[J]. King Saud University,2021,13(16).
APA	Namous, Mustapha.,Hssaisoune, Mohammed.,Pradhan, Biswajeet.,Lee, Chang-Wook.,Alamri, Abdullah.,...&Tagma, Tarik.(2021).Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models.WATER,13(16).
MLA	Namous, Mustapha,et al."Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models".WATER 13.16(2021).