Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis
The Mahanadi River Basin, a vital and largest river of Odisha, faces increasing surface water quality deterioration due to anthropogenic activities. The sustainable management of surface water is made more difficult by the dearth of thorough research on its chemical composition and the geochemical m...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Desalination and Water Treatment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1944398625003200 |
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| Summary: | The Mahanadi River Basin, a vital and largest river of Odisha, faces increasing surface water quality deterioration due to anthropogenic activities. The sustainable management of surface water is made more difficult by the dearth of thorough research on its chemical composition and the geochemical mechanisms affecting its hydrochemistry. The research aims to fill the gap by considering a total of 13 surface water samples, which were collected and analyzed using standard methods. Evaluating the effectiveness and dependability of classification algorithms in identifying changes in water quality is crucial since accurate information is required to improve decision-making. This study sought to evaluate the region's surface water quality and sources of contamination using machine learning (ML) methods such as Logistic Regression (LOR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN). Two indicators, including turbidity and coliform, exceeded the WHO guidelines, according to the samples' hydrochemistry study. The study finds distinct trends in the concentrations of cations and anion. The mean concentration of cations is rated as Mg2 + > Ca2+ > Na+ > K+, while, the major anions are typically showing a ranking of HCO3- > Cl- > SO42- > NO3-. The geochemical processes influencing the chemistry of surface waters are primarily driven by the weathering of silicate minerals. An increase in mineralization, is particularly important for magnesium silicates and carbonates. Various water quality indices (WQIs), implementing machine learning’s models including ANN, SVM, DT, RF, LOR), and KNN, jointly revealed that 36.36 % of samples reflects excellent to good water quality, while 63.64 % renders water quality status as unsuitable, which were mostly found in the central, south-eastern, and northeastern areas. Subsequently, geogenic and anthropogenic influences have a considerable impact on the surface water chemistry at seven water locations, according to the various ML analyses. According to the results, turbidity, TDS, TH, and coliform are important factors that shape different clusters of water quality. The random forest (RF) model with the highest accuracy and superior performance, that pertains to a score of R2 as 0.986, was being referred as best prediction model, while logistic regression (LOR) corresponds to R2 = 0.98, KNN as (R2 = 0.968), ANN as (R2 = 0.955), and finally, SVM includes R2 = 0.928. Basically, all approaches cumulatively predicted surface WQI with lower accuracy. Overall, the LOR, KNN, ANN, and SVM algorithms were all surpassed by the RF algorithm. Additionally, the prediction and simulation results show an increasing tendency in the changes in water quality over the examined time. This study presents the first comprehensive description of the composition of the surface water in the studied area, demonstrates the effectiveness of improved methods for interpreting intricate data sets to understand the dynamics of water quality, and creating a foundation for improved surface water management in this complex system of volcanic aquifers. |
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| ISSN: | 1944-3986 |