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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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Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model remote sensing Article Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model Samy Elmahdy 1 , Tarig Ali 1 and Mohamed Mohamed 2,3,* 1 GIS and Mapping Laboratory, Civil Engineering Department, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates; [email protected] (S.E.); [email protected] (T.A.) 2 Civil and Environmental Engineering Department, United Arab Emirates University, Al-Ain P.O. Box 15551, United Arab Emirates 3 National Water and Energy Center, United Arab Emirates University, Al Ain, Abu Dhabi P.O. Box 15551, United Arab Emirates * Correspondence: [email protected] Abstract: 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, Citation: Elmahdy, S.; Ali, T.; the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation Mohamed, M. Regional Mapping of result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents Groundwater Potential in Ar Rub Al a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khali, Arabian Peninsula Using the Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Classification and Regression Trees Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as Model. Remote Sens. 2021, 13, 2300. indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps https://doi.org/10.3390/rs13122300 are effective at enhancing the mapping of groundwater potential over a very regional scale obtained Academic Editor: James Cleverly using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert. Received: 24 April 2021 Accepted: 7 June 2021 Keywords: Saudi Arabia; remote sensing; groundwater; paleochannels; Umm Al Hiesh; CART model Published: 11 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional affil- Groundwater is an important source in land development and human activities such as iations. agriculture and industry. Groundwater is one of the most important resources worldwide, especially in arid and semi-arid regions [1–8]. In arid and semi-arid regions, there has been excessive consumption of groundwater and scarce rainfall due to climate change and population growth [1,2]. These have led to sharp depletions in the groundwater table Copyright: © 2021 by the authors. and the quality. These problems represent the challenge of developing such regions and Licensee MDPI, Basel, Switzerland. are clearly demonstrated in the Arabian Peninsula countries, particularly Saudi Arabia This article is an open access article and the United Arab Emirates [1]. About 50% of these countries lay within the largest distributed under the terms and sand sea in the world, which is known as ARAK. It is the largest promising groundwater conditions of the Creative Commons aquifer in the world and sits on top of a vast amount of fossil water reserves [1–3]. The Attribution (CC BY) license (https:// hydrological setting of the ARAK is explored only from oil wells and geophysical reports creativecommons.org/licenses/by/ over a local scale due to its harsh weather, remote location and lack of rock outcrops [2,3]. 4.0/). Remote Sens. 2021, 13, 2300. https://doi.org/10.3390/rs13122300 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2300 2 of 23 However, the remote sensing data have proven to be excellent tools for mapping surface and near-surface paleochannels, drainage basins, zones of groundwater potential and for retrieval of soil moisture over a regional scale [4–8]. Groundwater potential zones can be defined as the areas that reserve a considerable amount of groundwater or fossil water [1–3]. Locally, a limited number of studies have been applied to map near-surface fluvial channels delineated paleolake beds and the fluvial channels in the Jafurah sand field and the ARAK from the Shuttle Imaging Radar (SIR-C) imageries using manual screen digitizing [9,10]. However, this traditional technique introduces bias and errors [11]. Sultan et al. (2008) [12] automatically mapped the major paleochannels using the Shuttle Radar Topographic Mission (SRTM) DEM. They investigated the evolution of the ARAK aquifer using geochemical information collected from groundwater wells. Several studies have been achieved to model groundwater potential in a GIS environment integrated with Frequency Ratio (FR), Logistic Regression (LR), Weights of Evidence (WofE), Information Value (IV) and Shannon’s Entropy (SE) [9–14]. Further studies have been applied to susceptibility map geohazard and groundwater using machine learning such as Boosted Regression Tree (BRT), Classification and Regression Tree (CART), and Random Forest (RF) [15–30]. The CART has been applied to predict pulmonary tuberculosis in hospitalized patients [31]. The results of these studies show that the CART has a high predictive ability and had better performance than multivariate models [23,27]. The CART saves some computer time and is superior to categorical predictors, so it can be used for mapping groundwater potential over a very regional scale [27]. Therefore, this study aims to explore the effectiveness of the machine learning of mapping over a very regional scale, along with the applicability of the CART for groundwater potential. The CART, which is used widely in the literature provides the basis for important algorithms such as bagged decision, random forest and boosted decision trees [32]. This study is a new application of the CART for mapping groundwater potential over a very regional scale and represents an advanced step to develop predictable regional maps of groundwater potential created using a single machine learning model in an arid region. This study fills the gap in the hydrological information and permits a better understanding of the hydrological setting of the ARAK. Understanding the hydrological setting is important for better hydrological management of the ARAK and exploration of additional sites of groundwater. 2. Description of the Study Area 2.1. Geography and Topographic The study area, which includes Ar Rub Al Khali (the Empty Quarter) mega-basin and its aquifer underneath the sand sheets, stretches from longitude 40◦5401500E to 56◦4102000E and latitude from 14◦1502000N to 25◦1102300N and covers an area of about 557,467 km2 (Figure1). The ARAK region includes the ARAK aquifer underneath the sand sheets and its adjoining mountainous areas (sources of recharge). The ARAK region occupies an area of about 1,173,973 km2 and its aquifer is covered by the largest sand dune hills deposited during the pluvial and interpluvial periods of the Tertiary and Quaternary [33–36]. It is one of the largest aquifers in the world underlying sand sea and extending among the Kingdom of Saudi Arabia, the United Arab Emirates, the Sultanate of Oman and the Republic of Yemen (Figure1). The ARAK aquifer is located within arid and semiarid regions, inaccessible terrain due to its harsh climatic conditions. It is characterized by a hot dry climate, and temperature ranges from 41 ◦C to 55 ◦C during summer and from 15 ◦C to 27 ◦C during winter. The monthly rainfall during summer is low and ranges from 0 to 14 mm and could reach above 700 mm during October over the adjoining mountainous areas such as Asser, Hejaz and Juf in the west and southwest (Figure2a). Other regions such as the Riyad hills (northeast) and Hadramout (south) and Oman (east) mountains experience waves of precipitation varying from 90 to 220 mm that is a function of groundwater recharge, which varies from 0% to 4% [37]. Remote Sens. 2021, 13, 2300 3 of 23 Similar to the Eastern Sahara, the aquifers have received a considerable amount of water during wet periods that prevailed during the Late Pleistocene and Late Quaternary, as indicated by lacustrine, evaporite (sabkha) and playa carbonate deposits and paleochannels that are very closely associated with terminal desiccation of paleolake events [5,28,38–47]. Topographically, the ARAK aquifer area is surrounded by mountainous areas with elevation ranges from 50 m near the Saudi Arabia and UAE borders to 3500 m in the Aseer mountains (Figure2b). The ARAK aquifer area is dotted by several paleolakes with depth ranges from 2 to 10 m, produced by cataclysmic rainfall and with no links with rivers, and their bed sediment presents no evidence of regular filling [48,49] (green points in Figure1b). These features were described as lacustrine calcareous and/or clay and silt. They were observed to be governed by the shallow water table, which endorses evaporation and resulting gypsum, calcite, and anhydrite [49]. 2.2. Geology and Hydrology The ARAK aquifer area consists of a thick stratigraphic succession of sedimentary rocks belonging to Cambrian to recent and overlying the Precambrian basement of rugged rocks that are cropped out in the Red Sea [50]. Historically, the Arabian Peninsula ex- perienced several tectonic events, creating several regional faults, folds, grabens and hydrological basins (Figure2b) [ 3].
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