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
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".