The most critical issue that was the main research interest is its groundwater quality which is vital for public health concerns. Groundwater is a significant worldwide water supply for diverse communities, especially in dryland regions. Groundwater quality assessment in desert systems is largely hindered by the lack of hydrological data and the remote location of desert Oases. This study provides a preliminary understanding of the influences of climate, land usage, and population growth on the groundwater quality in El-Farafra Oasis in the Western Desert in Egypt from 2000 to now. Therefore, the study’s main objective was to determine the extent of change in temporal water quality and the factors causing it. The present study integrates chemical analyses and geospatial modeling better to assess groundwater quality in the study area. A chemical analysis of thirty-one groundwater samples from wells representing each study area was carried out during three time periods (2000, 2010, and 2022). Several chemical properties of groundwater samples gathered from wells in the research area were analyzed. Furthermore, the groundwater quality trend from 2000 to the present was identified using three approaches: Wilcox and Schoeller Diagram in Aq.QA software, interpolation in the ArcGIS software, and Ground Water Quality Index (GWQI). Moreover, the influence of changing land usage on groundwater quality was studied, and it was found that the increase in agriculture and urbanization areas is linked to groundwater quality degradation. The findings revealed that the barren area in 2000, 2010, and 2022 was 371.7, 362.0, and 343.2 km², respectively, which indicates a substantial decrease of 6.2% within this research timeframe. In contrast, agriculture and human-made structures have expanded by 1.8%. Also, population growth has led to an increase in water consumption as the population has grown at a rate of 7.52% annually from 2000 to 2020. As the climatic condition increases from 2000 to 2022, these changes could extend to the water quality in shallow aquifers with increasing evaporation. Based on the water quality spatial model, it is found that, despite a declining tendency in the rate of precipitation and an expansion in agricultural areas and population growth, the water quality was still appropriate for human and farming consumption in large areas of the study area. The presented approach is applicable to the assessment of groundwater in desert regions in the Middle East area.

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.