Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. For effective decontamination, it is pivotal to identify regional pollution hotspots. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, hotspots, and evaluate associated health risks pertaining to nitrate and fluoride in the Al-Hassa region, KSA. The physicochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021–4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate health quotient (HQ) values indicated chronic human health risks ranging from low to severe, with infants being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The hazard index (HI) yielded moderate- to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the total hazard index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources, vulnerable regions, and human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia.

Flood risk assessment is an important tool for urban planning, land development, and hydrological analysis. The flood risks are very high in arid countries due to the nature of the rainfall resulting from thunderstorms and the land cover, which comprises mostly very dry arid soil. Several methods have been used to assess the flood risk, depending on various factors that affect the likelihood of occurrence. However, the selection of these factors and the weight assigned to them remain rather arbitrary. This study assesses the risk of flood occurrence in arid regions based on land cover, soil type, precipitation, elevation, and flow accumulation. Thematic maps of the aforementioned factors for the study area were prepared using GIS. The Fuzzy Analytic Hierarchy Process (F-AHP) was used to calculate the likelihood of the flood occurrence, and land use was used to assess the exposure impact. Using the likelihood map (i.e., probability) from the Fuzzy-AHP and an exposure map, the flood risk was assessed. This method was applied to Qatar as a case study. Results were compared with those produced by fuzzy logic. To explore the pairwise importance of the F-AHP, equal weight analysis was performed. The resulting risk map shows that the majority of urbanized areas in Qatar are within the high-risk zone, with some smaller parts within the very high flood-risk area. The majority of the country is within the low-risk zone. Some areas, especially land depressions, are located within the intermediate-risk category. Comparison of Fuzzy logic and the F-AHP showed that both have similarities in the low-risk and differences in the high-risk zones. This reveals that the F-AHP is probably more accurate than other methods as it accounts for higher variability.