干旱区生态环境知识资源中心(Arid): Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq

Arid

DOI	10.3390/w13233330
	Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq
	Al-Ozeer, Ali ZA.; Al-Abadi, Alaa M.; Hussain, Tariq Abed; Fryar, Alan E.; Pradhan, Biswajeet; Alamri, Abdullah; Abdul Maulud, Khairul Nizam
通讯作者	Pradhan, B (corresponding author), Univ Technol, Sch Civil & Environm Engn, Fac Engn & IT, 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.
来源期刊	WATER
EISSN	2073-4441
出版年	2021
卷号	13 期号:23
英文摘要	Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms-namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)-were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to estimate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential.
英文关键词	cloud computing groundwater potential mapping GIS machine learning Iraq
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000734634900001
WOS关键词	HIERARCHY PROCESS ; GIS ; ACCURACY ; REGION ; BASIN
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373945
作者单位	[Al-Ozeer, Ali ZA.] Univ Mosul, Dept Geol, Coll Sci, Mosul 41001, Iraq; [Al-Ozeer, Ali ZA.; Al-Abadi, Alaa M.] Univ Basrah, Dept Geol, Coll Sci, Basrah 61004, Iraq; [Hussain, Tariq Abed] Univ Technol Baghdad, Dept Civil Engn, Baghdad 10001, Iraq; [Fryar, Alan E.] Univ Kentucky, Dept Earth & Environm Sci, Lexington, KY 40506 USA; [Pradhan, Biswajeet] Univ Technol, Sch Civil & Environm Engn, Fac Engn & IT, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Sydney, NSW 2007, Australia; [Pradhan, Biswajeet; Abdul Maulud, Khairul Nizam] Univ Kebangsaan Malaysia, Inst Climate Change, Earth Observat Ctr, Bangi 43600, Selangor, Malaysia; [Alamri, Abdullah] King Saud Univ, Dept Geol & Geophys, Coll Sci, Riyadh 11362, Saudi Arabia; [Abdul Maulud, Khairul Nizam] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Civil Engn, Bangi 43600, Selangor, Malaysia
推荐引用方式 GB/T 7714	Al-Ozeer, Ali ZA.,Al-Abadi, Alaa M.,Hussain, Tariq Abed,et al. Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq[J],2021,13(23).
APA	Al-Ozeer, Ali ZA..,Al-Abadi, Alaa M..,Hussain, Tariq Abed.,Fryar, Alan E..,Pradhan, Biswajeet.,...&Abdul Maulud, Khairul Nizam.(2021).Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq.WATER,13(23).
MLA	Al-Ozeer, Ali ZA.,et al."Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq".WATER 13.23(2021).