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

Sahour, Hossein; Sultan, M.; Abdellatif, Bassam; Emil, M. K.; Abotalib, Abotalib Z.; Abdelmohsen, Karem; Vazifedan, Mehdi; Mohammad, Abdullah T.; Hassan, Safaa M.; Metwalli, Mohamed R.; Bastawesy, Mohammed El

doi:10.1016/j.jhydrol.2022.128509

Cited by 16 publications

(9 citation statements)

References 94 publications

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“…It trains each tree in a sequential manner, where each subsequent tree corrects the errors made by the previous trees, resulting in a more accurate prediction. It uses a combination of gradient boosting and regularization techniques to improve model performance (Sahour et al, 2022). An extended Gradient Boost Machine is called XGBoost, which is an ensemble improving algorithm.…”

Section: Regressionmentioning

confidence: 99%

“…Based on the values of a collection of explanatory factors, LR is a useful model for determining whether an outcome will occur or not (Sahour et al, 2022). Multiple Linear Regression MLR is a regression algorithm used to model the relationship between multiple input variables and a continuous target variable (Ibrahimi et al, 2017).…”

Section: Regressionmentioning

confidence: 99%

“…These algorithms have been explored by authors in different regions of Africa using different set of variables. The input variables used in the studies depends mostly on data availability of the area and also on the understanding of the study area's geologic setting (Sahour et al, 2022).…”

Section: Regressionmentioning

confidence: 99%

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A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

Touré,

Boateng,

Gidigasu

et al. 2024

Preprint

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Groundwater is crucial for Africa's potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over exploitation. Over the past ten years, machine learning has been increasingly and successfully used in groundwater level prediction across the world. This review paper explores the application of machine learning techniques in predicting groundwater levels in Africa. The methodology involved downloading relevant papers, identifying and categorizing the machine learning algorithms employed, and quantifying their use. Geological and climatic variables were also identified, analyzed and categorized to measure their usage frequency. The different algorithms and input variables extracted from each paper are graphically represented in this document highlighting the most employed ones. The findings suggest that the available literature on this topic in Africa is limited compared to the rest of the world. Tree-based algorithms are commonly used in machine learning in Africa, and the most employed input variables are related to geomorphology and temperature. The study highlights the potential of machine learning in improving water resource management and decision-making in the region.

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Section: Regressionmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

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A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

Touré,

Boateng,

Gidigasu

et al. 2024

Preprint

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“…This role can be detected through their accompanying thermal anomaly. A better understanding of the tectonic history of the Gulf of Suez could additionally help the efforts of assigning the tectonic-climatic relationship [46][47][48][49], evaluating the tectonically shaped hydrocarbon basins [50][51][52], delineating structurally controlled aquifers in Egypt [53][54][55], and assessing the geohazards in the newly developed cities within the Gulf of Suez region [56,57]. A better understanding of the tectonic history of the Gulf of Suez could additionally help the efforts of assigning the tectonic-climatic relationship [46][47][48][49], evaluating the tectonically shaped hydrocarbon basins [50][51][52], delineating structurally controlled aquifers in Egypt [53][54][55], and assessing the geohazards in the newly developed cities within the Gulf of Suez region [56,57].…”

Section: Introductionmentioning

confidence: 99%

Tectonic-Thermal Evolution of the Wadi El-Dahal Area, North Eastern Desert, Egypt: Constraints on the Suez Rift Development

Mansour,

Hasebe,

Khedr

et al. 2023

Minerals

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The Suez Rift developed as a northern extension of the Red Sea rift during the Oligocene-Miocene, whose flanks were constructed from the Neoproterozoic basement rocks of the Arabian–Nubian Shield. These basement rocks are comprised of the whole tectonic history since their formation. The Suez Rift initiation model and proposed thermal overprint role in the rifting process and flank development remain uncertain. Additionally, the amplitude of different regional tectonic events’ effects on the region is still debatable. Integration of fission-track thermochronology data with modeling of the time-temperature history has demonstrated efficiency in addressing such issues. In the context of this study, eleven representative samples were collected from the different rock units in the Wadi El-Dahal area at the northern tip of the western flank of the Suez Rift. These samples revealed Carboniferous zircon fission-track cooling ages of 353 ± 9 Ma and 344 ± 11 Ma. Meanwhile, the apatite fission-track analysis provided two spatially separated age groups: Permian-Triassic and Late Cretaceous, with average ages of 249 ± 11 Ma and ca. 86 ± 10 Ma, respectively. The time-temperature modeling revealed four possible cooling pulses representing exhumation events, which were initiated as a response to four tectonic activities: the accretion-subsequent event of erosion during the Neoproterozoic, the Hercynian (Variscan) tectonic event during the Devonian-Carboniferous, the Mid-Atlantic opening during the Cretaceous, and the Suez Rift opening during the Oligocene-Miocene. The western flank of the Suez Rift suggests a passive mechanical type with no extra thermal overprint, as indicated by the dominance of older thermochronological ages, modest rift flank elevations, and a reduction in the heat flow.

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“…The Gravity Recovery and Climate Experiment (GRACE) mission [17] was launched on the 17 March 2002 and continued until the 27 October 2017. GRACE became an essential source of data for continuously monitoring the variation in water fluxes over large basins, e.g., in Africa (e.g., Nubian and Nile basin, [9,10,[18][19][20], in Saudi Arabia (e.g., Mega Aquifer [21][22][23]), and in Asia (Tigris Euphrates [24]), in USA (Michigan [25]). The GRACE Follow-On (GRACE-FO) mission was a successive mission launched on the 22nd of May 2018, and it continues to provide accurate estimates of the mean and time-variable components of the gravity field and its corresponding variations in water fluxes, as well the TWS over large basins at a spatial resolution of about 200 km (corresponding to the spherical harmonics expansion of degree 96) over monthly time spans.…”

Section: Introductionmentioning

confidence: 99%

Mass Variations in Terrestrial Water Storage over the Nile River Basin and Mega Aquifer System as Deduced from GRACE-FO Level-2 Products and Precipitation Patterns from GPCP Data

Elsaka

Abdelmohsen

Alshehri

et al. 2022

Water

Self Cite

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Changes in the terrestrial total water storage (TWS) have been estimated at both global and river basin scales from the Gravity Recovery and Climate Experiment (GRACE) mission and are still being detected from its GRACE Follow-On (GRACE-FO) mission. In this contribution, the sixth release of GRACE-FO (RL06) level-2 products applying DDK5 (decorrelation filter) were used to detect water mass variations for the Nile River Basin (NRB) in Africa and the Mega Aquifer System (MAS) in Asia. The following approach was implemented to detect the mass variation over the NRB and MAS: (1) TWS mass (June 2018–June 2021) was estimated by converting the spherical harmonic coefficients from the decorrelation filter DDK 5 of the GRACE-FO Level-2 RL06 products into equivalent water heights, where the TWS had been re-produced after removing the mean temporal signal (2) Precipitation data from Global Precipitation Climatology Project was used to investigate the pattern of change over the study area. Our findings include: (1) during the GRACE-FO period, the mass variations extracted from the RL06-DDK5 solutions from the three official centers—CSR, JPL, and GFZ—were found to be consistent with each other, (2) The NRB showed substantial temporal TWS variations, given a basin average of about 6 cm in 2019 and about 12 cm in 2020 between September and November and a lower basin average of about −9 cm in 2019 and −6 cm in 2020 in the wet seasons between March and May, while mass variations for the MAS had a relatively weaker temporal TWS magnitude, (3) the observed seasonal signal over the NRB was attributed to the high intensity of the precipitation events over the NRB (AAP: 1000–1800 mm yr−1), whereas the lack of the seasonal TWS signal over the MAS was due to the low intensity of the precipitation events over the MAS (AAP: 180–500 mm yr−1).

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

Cited by 16 publications

References 94 publications

A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

Tectonic-Thermal Evolution of the Wadi El-Dahal Area, North Eastern Desert, Egypt: Constraints on the Suez Rift Development

Mass Variations in Terrestrial Water Storage over the Nile River Basin and Mega Aquifer System as Deduced from GRACE-FO Level-2 Products and Precipitation Patterns from GPCP Data

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