article · Environmental Science and Pollution Research
Assessing groundwater quality is vital for sustaining economic and social activities in water-stressed arid zones. Research conducted in the Abu-Sweir and Abu-Hammad areas of Ismailia, Egypt, evaluated groundwater conditions by integrating support vector machines with traditional water quality indices. Using field data and multiple water quality parameters as predictors, the combined approach achieved an accuracy of 0.90, outperforming the standalone machine learning model which reached an accuracy of 0.88. The integrated method classified 68 percent of the examined samples as permissible and 15 percent as unsuitable, while identifying fewer areas of excellent quality compared to individual assessment tools. Analysis of the resulting data confirmed that local groundwater conditions are significantly governed by natural rock-water interactions, dissolution, and leaching. This predictive modelling approach provides clearer insight into groundwater status to inform future development initiatives in arid regions.
Securing clean water in arid environments is critical for community health and local economies. Combining artificial intelligence with standard water quality metrics allows authorities to evaluate groundwater safety with greater precision. Understanding how natural processes like mineral dissolution affect water quality helps resource managers make evidence-based decisions about which water sources are safe for consumption and agricultural development.
The methodology could form the analytical core of digital environmental monitoring tools for hydrological engineers, municipal water planners, and environmental protection agencies. Tested on field data from Egypt, the approach sits at an applied and tested stage of development. Moving towards practical deployment would require embedding the predictive models into commercial environmental software or regional water management platforms.
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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.
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DOI: 10.1007/s11356-023-25938-1
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