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Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

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GIS and Mapping Laboratory, Civil Engineering Department, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
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Civil and Environmental Engineering Department, United Arab Emirates University, Al-Ain P.O. Box 15551, United Arab Emirates
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National Water and Energy Center, United Arab Emirates University, Al Ain, Abu Dhabi P.O. Box 15551, United Arab Emirates
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Author to whom correspondence should be addressed.
Academic Editor: James Cleverly
Remote Sens. 2021, 13(12), 2300; https://doi.org/10.3390/rs13122300
Received: 24 April 2021 / Revised: 4 June 2021 / Accepted: 7 June 2021 / Published: 11 June 2021
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert. View Full-Text
Keywords: Saudi Arabia; remote sensing; groundwater; paleochannels; Umm Al Hiesh; CART model Saudi Arabia; remote sensing; groundwater; paleochannels; Umm Al Hiesh; CART model
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MDPI and ACS Style

Elmahdy, S.; Ali, T.; Mohamed, M. Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model. Remote Sens. 2021, 13, 2300. https://doi.org/10.3390/rs13122300

AMA Style

Elmahdy S, Ali T, Mohamed M. Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model. Remote Sensing. 2021; 13(12):2300. https://doi.org/10.3390/rs13122300

Chicago/Turabian Style

Elmahdy, Samy, Tarig Ali, and Mohamed Mohamed. 2021. "Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model" Remote Sensing 13, no. 12: 2300. https://doi.org/10.3390/rs13122300

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