Identification of shallow groundwater in arid lands using multi-sensor remote sensing data and machine learning algorithms

•Locate shallow groundwater occurrences.•Test site: Western Desert (area: ∼ 680,000 km2) in Egypt.•Integrated (remote sensing, machine learning, GIS, field, geochemistry) approach.•Pressurized groundwater access deep-seated faults and discharge at the near-surface. The focus of this study is to loca...

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Published in:Journal of hydrology (Amsterdam) 2022-11, Vol.614, p.128509, Article 128509
Main Authors: Sahour, Hossein, Sultan, Mohamed, Abdellatif, Bassam, Emil, Mustafa, Abotalib, Abotalib Z., Abdelmohsen, Karem, Vazifedan, Mehdi, Mohammad, Abdullah T., Hassan, Safaa M., Metwalli, Mohamed R., El Bastawesy, Mohammed
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creator Sahour, Hossein
Sultan, Mohamed
Abdellatif, Bassam
Emil, Mustafa
Abotalib, Abotalib Z.
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Mohammad, Abdullah T.
Hassan, Safaa M.
Metwalli, Mohamed R.
El Bastawesy, Mohammed
description •Locate shallow groundwater occurrences.•Test site: Western Desert (area: ∼ 680,000 km2) in Egypt.•Integrated (remote sensing, machine learning, GIS, field, geochemistry) approach.•Pressurized groundwater access deep-seated faults and discharge at the near-surface. The focus of this study is to locate shallow groundwater (SGW) occurrences in arid lands using the Western Desert (WD; area: ∼680,000 km2) of Egypt as a test site. The SGW in the study area originated from paleo-precipitation during previous wet climatic periods. In wet periods, fossil groundwater was at higher levels, ascended along high-angle faults, and discharged at the surface. In contrast, at present, the water levels are lower, and the discharge occurs at near-surface elevations. Spring locations were identified as the dependent variable, while the independent variables included remote sensing–based variables and geomorphological features indicative of current or paleo discharge locations, including elevation, slope, curvature, distance to sapping features, soil moisture, NDVI, radar backscatter coefficient, and brightness temperature. Relationships between SGW occurrences (target) and their controlling factors (independent variables) were established using extreme gradient boosting (XGB), support vector machine (SVM), and logistic regression (LR) methods. The trained models were used to map SGW locations across the entire WD. Findings include the following: (1) the XGB yielded the most favorable result in identifying SGW locations (overall accuracy: 0.93) compared to SVM (overall accuracy: 0.88) and LR (overall accuracy: 0.87); (2) areas with a very high probability of SGW occurrences were found in lowlands and proximal to sapping features; (3) the overwhelming majority of the cultivated lands within the southern and central sections of the WD lie within areas identified as high and very high probability SGW locations; (4) our models identify-two previously unrecognized major SGW occurrences, an eastern zone (EZ; length: 800 km; width: 9 to 80 km; area: 43,000 km2) and an east–west trending northern zone (NZ) centered over the Qattara depression (length: 500 km; width: 200 km; area: 62,150 km2); (5) additional criteria were used to refine the modeled (XGB) SGW distribution (southern and central WD: 43,200 km2 to 23,400 km2; EZ: from 21,300 km2 to 17,400, and NZ: from 62,150 to 30,700 km2) including presence of shallow aquifers to accommodate rising Nubian waters, Nubian water salinity (f
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The focus of this study is to locate shallow groundwater (SGW) occurrences in arid lands using the Western Desert (WD; area: ∼680,000 km2) of Egypt as a test site. The SGW in the study area originated from paleo-precipitation during previous wet climatic periods. In wet periods, fossil groundwater was at higher levels, ascended along high-angle faults, and discharged at the surface. In contrast, at present, the water levels are lower, and the discharge occurs at near-surface elevations. Spring locations were identified as the dependent variable, while the independent variables included remote sensing–based variables and geomorphological features indicative of current or paleo discharge locations, including elevation, slope, curvature, distance to sapping features, soil moisture, NDVI, radar backscatter coefficient, and brightness temperature. Relationships between SGW occurrences (target) and their controlling factors (independent variables) were established using extreme gradient boosting (XGB), support vector machine (SVM), and logistic regression (LR) methods. The trained models were used to map SGW locations across the entire WD. Findings include the following: (1) the XGB yielded the most favorable result in identifying SGW locations (overall accuracy: 0.93) compared to SVM (overall accuracy: 0.88) and LR (overall accuracy: 0.87); (2) areas with a very high probability of SGW occurrences were found in lowlands and proximal to sapping features; (3) the overwhelming majority of the cultivated lands within the southern and central sections of the WD lie within areas identified as high and very high probability SGW locations; (4) our models identify-two previously unrecognized major SGW occurrences, an eastern zone (EZ; length: 800 km; width: 9 to 80 km; area: 43,000 km2) and an east–west trending northern zone (NZ) centered over the Qattara depression (length: 500 km; width: 200 km; area: 62,150 km2); (5) additional criteria were used to refine the modeled (XGB) SGW distribution (southern and central WD: 43,200 km2 to 23,400 km2; EZ: from 21,300 km2 to 17,400, and NZ: from 62,150 to 30,700 km2) including presence of shallow aquifers to accommodate rising Nubian waters, Nubian water salinity (fresh to brackish), and low to moderate thickness (&lt;1 km) of post-Nubian successions that rising waters interact with. 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The focus of this study is to locate shallow groundwater (SGW) occurrences in arid lands using the Western Desert (WD; area: ∼680,000 km2) of Egypt as a test site. The SGW in the study area originated from paleo-precipitation during previous wet climatic periods. In wet periods, fossil groundwater was at higher levels, ascended along high-angle faults, and discharged at the surface. In contrast, at present, the water levels are lower, and the discharge occurs at near-surface elevations. Spring locations were identified as the dependent variable, while the independent variables included remote sensing–based variables and geomorphological features indicative of current or paleo discharge locations, including elevation, slope, curvature, distance to sapping features, soil moisture, NDVI, radar backscatter coefficient, and brightness temperature. Relationships between SGW occurrences (target) and their controlling factors (independent variables) were established using extreme gradient boosting (XGB), support vector machine (SVM), and logistic regression (LR) methods. The trained models were used to map SGW locations across the entire WD. Findings include the following: (1) the XGB yielded the most favorable result in identifying SGW locations (overall accuracy: 0.93) compared to SVM (overall accuracy: 0.88) and LR (overall accuracy: 0.87); (2) areas with a very high probability of SGW occurrences were found in lowlands and proximal to sapping features; (3) the overwhelming majority of the cultivated lands within the southern and central sections of the WD lie within areas identified as high and very high probability SGW locations; (4) our models identify-two previously unrecognized major SGW occurrences, an eastern zone (EZ; length: 800 km; width: 9 to 80 km; area: 43,000 km2) and an east–west trending northern zone (NZ) centered over the Qattara depression (length: 500 km; width: 200 km; area: 62,150 km2); (5) additional criteria were used to refine the modeled (XGB) SGW distribution (southern and central WD: 43,200 km2 to 23,400 km2; EZ: from 21,300 km2 to 17,400, and NZ: from 62,150 to 30,700 km2) including presence of shallow aquifers to accommodate rising Nubian waters, Nubian water salinity (fresh to brackish), and low to moderate thickness (&lt;1 km) of post-Nubian successions that rising waters interact with. 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The focus of this study is to locate shallow groundwater (SGW) occurrences in arid lands using the Western Desert (WD; area: ∼680,000 km2) of Egypt as a test site. The SGW in the study area originated from paleo-precipitation during previous wet climatic periods. In wet periods, fossil groundwater was at higher levels, ascended along high-angle faults, and discharged at the surface. In contrast, at present, the water levels are lower, and the discharge occurs at near-surface elevations. Spring locations were identified as the dependent variable, while the independent variables included remote sensing–based variables and geomorphological features indicative of current or paleo discharge locations, including elevation, slope, curvature, distance to sapping features, soil moisture, NDVI, radar backscatter coefficient, and brightness temperature. Relationships between SGW occurrences (target) and their controlling factors (independent variables) were established using extreme gradient boosting (XGB), support vector machine (SVM), and logistic regression (LR) methods. The trained models were used to map SGW locations across the entire WD. Findings include the following: (1) the XGB yielded the most favorable result in identifying SGW locations (overall accuracy: 0.93) compared to SVM (overall accuracy: 0.88) and LR (overall accuracy: 0.87); (2) areas with a very high probability of SGW occurrences were found in lowlands and proximal to sapping features; (3) the overwhelming majority of the cultivated lands within the southern and central sections of the WD lie within areas identified as high and very high probability SGW locations; (4) our models identify-two previously unrecognized major SGW occurrences, an eastern zone (EZ; length: 800 km; width: 9 to 80 km; area: 43,000 km2) and an east–west trending northern zone (NZ) centered over the Qattara depression (length: 500 km; width: 200 km; area: 62,150 km2); (5) additional criteria were used to refine the modeled (XGB) SGW distribution (southern and central WD: 43,200 km2 to 23,400 km2; EZ: from 21,300 km2 to 17,400, and NZ: from 62,150 to 30,700 km2) including presence of shallow aquifers to accommodate rising Nubian waters, Nubian water salinity (fresh to brackish), and low to moderate thickness (&lt;1 km) of post-Nubian successions that rising waters interact with. The techniques are cost-effective and efficient and could be readily applied to large sectors of Saharan Africa and Arabia, whose landscape and fossil aquifers bear many resemblances in their geologic, climatic, and geomorphic characteristics to the Nubian Sandstone Aquifer System.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.jhydrol.2022.128509</doi><oa>free_for_read</oa></addata></record>
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source Elsevier
subjects aquifers
Arid Lands
cost effectiveness
Egypt
fossils
geomorphology
groundwater
landscapes
Machine Learning Algorithms
probability
radar
regression analysis
Remote Sensing
sandstone
Shallow Groundwater
soil water
support vector machines
temperature
water salinity
Western Desert
title Identification of shallow groundwater in arid lands using multi-sensor remote sensing data and machine learning algorithms
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