Flooding is a natural disaster with extensive impacts. Desert regions face altered flooding patterns owing to climate change, water scarcity, regulations, and rising water demands. This study assessed and predicted flash flood hazards by calculating discharge volume, peak flow, flood depth, and velocity using the Hydrologic Engineering Centre-River Analysis System and Hydrologic Modelling System (HEC-HMS and HEC-RAS) software. We employed meteorological and morphological data analyses, incorporating the soil conservation service (SCS) curve number method for precipitation losses and the SCS-Hydrograph for runoff transformation. The model was applied to two drainage basins (An-Nawayah and Al-Rashrash) in southeastern Cairo, Egypt, which recently encountered several destructive floods. The applied model revealed that 25-, 50-, and 100-year storms produced runoff volumes of 2461.8 × 10³, 4299.6 × 10³, and 5204.5 × 10³ m³ for An-Nawayah and 6212 × 10³, 8129.4 × 10³, and 10,330.6 × 10³ m³ for Al-Rashrash, respectively. Flood risk levels, categorised as high (35.6%), extreme (21.9%), and medium (21.12%) were assessed in low- and very-low-hazard areas. The study highlighted that the areas closer to the Nile River mouth faced greater flood impacts from torrential rain. Our findings demonstrate the effectiveness of these methods in assessing and predicting flood risk. As a mitigation measure, this study recommends the construction of five 10 m high dams to create storage lakes. This integrated approach can be applied to flood risk assessment and mitigation in comparable regions.

Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson’s ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world.

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments.

Machine Learning Techniques (MLTs) and accurate geographic mapping are crucial for managing natural hazards, especially when monitoring the movement of sand dunes. This study presents the integration of MLTs with geographic information systems (GIS) and “R” software to monitor sand dune movement in Najran City, Saudi Arabia (KSA). Utilizing Linear Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN) with nine dune-related variables, this study introduces a new Drifting Sand Index (DSI) for effectively identifying and mapping dune accumulations. The DSI incorporates multispectral sensors data and demonstrates a robust capability for monitoring sand dune dynamics. Field surveys and spatial data analysis were used to identify about 100 dune locations, which were then divided into training (70%) and validation (30%) sets at random. These models produced a thorough dune encroachment risk map that divided areas into five hazard zones: very low, low, medium, high, and very high risk. The results show an average sand dune movement of 0.8 m/year towards the southeast. Performance evaluation utilizing the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) approach revealed AUC values of 96.2% for SVM, 94.2% for RF, and 93% for ANN, indicating RF (AUC = 96.2%) as the most effective MLTs. This crucial information provides valuable insights for sustainable development and environmental protection, enabling decision-makers to prioritize regions for mitigation techniques against sand dune encroachment.