Harnessing hydro chemical characterization of surface water using water quality indices and machine learning – Driven water quality modelling with special emphasis on side – Stream pollution

Abstract

The increasing contamination of river systems due to rapid urbanization, industrial discharge and agricultural runoff poses a serious threat to environmental and public health. Assessing drinking water sources' safety and sustainability in particular, surface water, was the main goal. The current work aimed to assess the water quality using different water quality index (WQI) methods namely, Weighted Arithmetic (WA), British Columbia (BC), Canadian Council of Ministers of the Environment (CCME), and Entropy – Weighted (E) - WQI. Also, the present study applies advanced machine learning (ML) models to assess and predict the water quality index (WQI) in the Mahanadi River and its distributaries of Paradip area, during the pre-monsoon season, a period with minimal dilution effects. The WA-WQI findings revealed water quality “good to unsuitable” category, with an average of 90.16. In contrast, BC-WQI exhibited a reported score of 11 – 97, indicating 22.22% of samples rendering good – fair water classification. The computed results of WQI (15 – 84.67), underscore the potential of CCME, signifying 66.66% of tested samples classified under marginal – poor water category. The results show notable geographical diversity in water quality, with EWQI values ranging from 195 at Ms -7 (which implies severe contamination) to 39 at Ms -1 (displaying relatively better conditions). The study compares the performance of multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM), and random forest model (RFM), respectively. Results showed that ANN achieved the highest predictive accuracy (92.60 of predictions with 20% of actual WQI), followed by RFM (79.44%) while MLR and SVM showed limited performance. This study demonstrates the potential of ML – based models for accurate water quality prediction, supports data – driven strategies for sustainable water resource management, offering globally applicable insights for water conservation while being in line with the Sustainable Development Goals (SDGs) pertaining to clean water and ecosystem restoration. • Surface water potential is vital for managing scarce water resources in polluted areas. • Mahanadi River and its distributaries of Paradip area, faces water scarcity with drinking and agriculture as its main economic activity. • WQIs – ML integration enhances the reliability of surface water potential mapping. • Results concluded ANN model outperforms other MLP, SVM, and RFM in accuracy for performance assessment. • Combining datasets improves factor evaluation for surface water availability mapping. • This proactive monitoring enhances public health and safeguards communities from potential hazards.

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