Groundwater (GW) represents a critical resource for sustaining agriculture and rural communities across the arid regions of many developing countries. This study assesses three predictive approaches boosted classification tree (BCT), the bivariate frequency ratio (FR), and a hybrid BCT–FR ensemble for mapping Potential Zones (GWPZ) in arid environments. The modelling framework integrates satellite-derived variables with pumping-test measurements (specific capacity (SPC) and transmissivity (T)) and incorporates topographic, geological, hydrogeological, and anthropogenic factors using an inventory of forty-two wells divided into calibration (70%) and validation (30%) datasets across the West El-Minia region of Upper Egypt. Change-detection analysis over the study period (2000–2025) indicated a substantial increase in agricultural activity, with cultivated lands expanding by more than 560 km². This expansion was accompanied by an observed level decline of approximately five meters over the same period, based on field measurements from 42 wells and calculated using observed water table differences. Based on SPC predictions, the BCT and hybrid FR–BCT models achieved relatively high area under the curve (AUC) values. For transmissivity, the corresponding accuracy values were 83.38% for BCT and 92.58% for FR–BCT. Model outputs were assessing their reliability by comparing the GW potential map generated with available borehole information and daily GWproductivity data from the aquifer system. The study area was classified into four GW potential categories: very high (8%), high (26%), moderate (54%), and low (12%), with the northeastern sector exhibiting the highest recharge and storage potential. Overall, the applied machine-learning techniques demonstrated good performance for GW potential assessment in data-limited environments. The results provide important guidance for GW resource management by identifying zones with substantial recharge and development potential.
A. Farrag, Nourham M. Sayed, Amnah Aldohan et al.· Environmental Earth Sciences· 0 citations
The groundwater distribution in southern Egypt is primarily governed by the region’s inherited rift-related tectonic framework. We integrated aeromagnetic data with available hydrogeochemical and seismic data to investigate structural controls on groundwater accumulation in El-Gallaba Plain along the western margin of the Kom Ombo Basin. The magnetic data were processed using edge-detection filters and source-parameter imaging to estimate the depth of the basement rock. Moreover, three-dimensional geophysical inversion of magnetic data was performed using the iteratively re-weighted least-squares algorithm to map the basin geometry. The results of magnetic data interpretation delineated a NW-SE major trend and minor NE-SW-trending, fault-bounded depressions extending to depths of 2.5–3.0 km, interpreted as sediment-filled or altered-basement fractured basins favorable for groundwater accumulation. Uplifted basement blocks define structural highs that act as hydraulic barriers, compartmentalizing the area’s aquifer system. The interpreted magnetic structures were correlated with previously published land-surface temperature and hydrochemical data, revealing a spatial alignment among deep structural depressions, low-temperature corridors, and zones of fresher, isotopically depleted groundwater. This correlation confirms that reactivated Pan-African and Cenozoic faults control the vertical permeability and groundwater connectivity of the Nubian Sandstone Aquifer. Overall, this study highlights the effectiveness of three-dimensional inversion, along with edge-detection and depth-estimation filters applied to magnetic data to resolve subsurface architecture and interpret groundwater potential in arid, tectonically complex regions.
Mohammad A. Shehata, Tamer Attia, T. Farag et al.· Applied Water Science· 0 citations
Earthquakes represent one of the most destructive natural hazards, particularly in developing regions along the convergence zone of the Arabian and Eurasian tectonic plates, where they result in severe human casualties, extensive infrastructure damage, and major economic losses. This study applies an integrated methodology that combines geospatial data with a Geographic Information Systems (GIS)-based Analytic Hierarchy Process (AHP) to produce the first regional-scale, screening-level Seismic Hazard Zonation (SHZ) map for the Kurdistan Region of Iraq (KRI), situated at this tectonic boundary. Eight governing factors, including distance to active faults, tectonic lineaments, lithological characteristics, soil properties, earthquake magnitude, seismic frequency, and focal depth, were analyzed and weighted according to their relative influence on seismic hazard potential. Validation using the Frequency Ratio (FR) approach demonstrated FR values below 1 in low-susceptibility areas, approximately 1 in moderate-hazard zones, above 1 in the high hazard class (FR = 1.66), and markedly elevated in the very high hazard class (FR = 9.27), indicating a positive spatial association between the modeled hazard distribution and recorded seismic events; the latter value is partly inflated by the small areal extent of the very high class and the spatial clustering of events within it. The analysis categorized KRI into five hazard levels: very low, low, moderate, high, and very high. Approximately 2,284 km
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(4.9%) of the region falls within the very high hazard class, highlighting the urgent need for site-specific mitigation measures and disaster preparedness strategies. Overall, this screening-level relative zonation provides a first-order spatial prioritization of seismic hazard in the KRI, supporting regional planning and guiding policymakers toward areas where detailed site-specific investigations, probabilistic seismic hazard analysis, and geotechnical characterization should be prioritized.
Kaifi Chomani, Shaki Pshdari, Rawshan Ali et al.· Scientific Reports· 0 citations
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