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      Mapping Groundwater Potential (GWP) in the Al-Ahsa Oasis, Eastern Saudi Arabia Using Data-Driven GIS Techniques

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      Water
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          Abstract

          Searching for new sources of water is becoming one of the most important aspects of scientific research, especially in areas prone to drought, like Saudi Arabia. The study aim was to delineate groundwater potential zones within the Oasis of Al-Ahsa, in Saudi Arabia’s eastern region, and to identify the optimum factors that control the availability of groundwater zones. This was achieved through examining the effect of ten environmental variables on groundwater recharge, namely: slope, topographic wetness index (TWI), land cover (LC), elevation, lineament density (Ld), drainage density (Dd), rainfall, geology, and soil texture. The variables were prepared from a variety of data sources, including spatial data (i.e., DEM and Landsat-8 image), in addition to other complementing data sources for appropriate parameters extraction. Two weighted overlay methods were used, namely the simple additive weight (SAW) as well as the optimum index factor (OIF) in order to categorize the optimal set of parameters for computing GWP and identifying its zones. Two GWP maps were obtained and validated through comparison with the locations of existing wells at GWP zones. The study findings have assured the cogency of the SAW map, where it was found that nearly 45–48% of the resultant zones were characterized as in the “moderate” class, whereas around 21–37% of the entire zones area were classified within the “high” class. The soil texture parameter was determined as being the most influencing parameter for GWP mapping followed by the “geology” parameter; however, the “lineament density” (Ld) was the least important factor. Furthermore, the OIF method has facilitated the identification of the optimal parameter combination for delineating groundwater potential (GWP) zones, which included “Ld”, “land cover”, and “TWI”. The study findings and methodology can serve as a potential model for other similar regions, supporting sustainable water resource management locally as well as globally.

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          Most cited references50

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          A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant

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            GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

            Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.
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              Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                WATEGH
                Water
                Water
                MDPI AG
                2073-4441
                January 2024
                January 05 2024
                : 16
                : 2
                : 194
                Article
                10.3390/w16020194
                82a00c5f-bcaf-4d0f-8fbc-e4f6f708112f
                © 2024

                https://creativecommons.org/licenses/by/4.0/

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