Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches.

The demands upon the arid area for water supply pose threats to both the quantity and quality of social and economic activities. Thus, a widely used machine learning model, namely the support vector machines (SVM) integrated with water quality indices (WQI), was used to assess the groundwater quality. The predictive ability of the SVM model was assessed using a field dataset for groundwater from Abu-Sweir and Abu-Hammad, Ismalia, Egypt. Multiple water quality parameters were chosen as independent variables to build the model. The results revealed that the permissible and unsuitable class values range from 36 to 27%, 45 to 36%, and 68 to 15% for the WQI approach, SVM method and SVM-WQI model respectively. Besides, the SVM-WQI model shows a low percentage of the area for excellent class compared to the SVM model and WQI. The SVM model trained with all predictors with a mean square error (MSE) of 0.002 and 0.41; the models that had higher accuracy reached 0.88. Moreover, the study highlighted that SVM-WQI can be successfully implemented for the assessment of groundwater quality (0.90 accuracy). The resulting groundwater model in the study sites indicates that the groundwater is influenced by rock-water interaction and the effect of leaching and dissolution. Overall, the integrated ML model and WQI give an understanding of water quality assessment, which may be helpful in the future development of such areas.

Spatial Distribution, Contamination Levels, and Health Risk Assessment of Potentially Toxic Elements in Household Dust in Cairo City, Egypt.

Urban areas' pollution, which is owing to rapid urbanization and industrialization, is one of the most critical issues in densely populated cities such as Cairo. The concentrations and the spatial distribution of fourteen potentially toxic elements (PTEs) in household dust were investigated in Cairo City, Egypt. PTE exposure and human health risk were assessed using the USEPA's exposure model and guidelines. The levels of As, Cd, Cr, Cu, Hg, Mo, Ni, Pb, and Zn surpassed the background values. Contamination factor index revealed that contamination levels are in the sequence Cd > Hg > Zn > Pb > Cu > As > Mo > Ni > Cr > Co > V > Mn > Fe > Al. The degree of contamination ranges from considerably to very high pollution. Elevated PTE concentrations in Cairo's household dust may be due to heavy traffic emissions and industrial activities. The calculated noncarcinogenic risk for adults falls within the safe limit, while those for children exceed that limit in some sites. Cairo residents are at cancer risk owing to prolonged exposure to the indoor dust in their homes. A quick and targeted plan must be implemented to mitigate these risks.

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact.

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.