Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.

Historic Cairo is particularly considered a UNESCO World Heritage site that is very vulnerable to natural disasters, such as flooding and landslides, which would threaten its cultural heritage. Multi-risk vulnerability assessments of heritage sites were investigated using remote sensing and GIS methods. Key factors were prioritized using the Analytic Hierarchy Process, and hazard maps were generated to identify areas at risk of floods and/or landslides. The investigation revealed that 54.1% of the study area falls within very low to moderate flood hazard zones, while 12.4% is classified as very high hazard, affecting 21 heritage sites. Additionally, 16% of the study area is in high-threat zones for landslides, impacting 13 cultural sites, with none in very high-risk zones. Considering both hazards, at least 30 sites are in high-risk areas, including 9 in very high-risk zones. The results highlight the urgent need to develop and implement targeted risk mitigation strategies, particularly for sites in central and eastern Historic Cairo, where flood and landslide risks overlap. The GIS-based database and hazard maps provide valuable tools for urban planners, policymakers, and stakeholders to formulate effective protection strategies for Historic Cairo’s cultural heritage.