Impact analysis of polluted waste water discharge in river and management process using machine learning and GIS approach
Bibliographic record
Abstract
Discharging untreated or inadequately purified wastewater into waterways disrupts ecosystems and threatens human health. Industrial activities are major contributors to river pollution, necessitating effective management strategies. This study integrates Machine Learning (ML) and Geographic Information System (GIS) techniques to assess wastewater discharge impacts and develop an optimized management framework using the Random Forest algorithm and GIS-based spatial analysis to identify pollution hotspots and high-impact industrial contributors. By evaluating ten key pollution factors (F1–F10), the study quantifies industries' relative contributions to contamination. Industries S5 (0.232) and S6 (0.225) emerge as the most significant polluters, requiring stricter regulations. GIS-based spatial analysis reveals pollution dispersion patterns, while a supervised machine learning approach using the Random Forest algorithm used to predict pollution levels and identify major industrial contributors. ML enhances decision-making by identifying high-risk areas and proposing adaptive controls, while GIS aids in visualizing pollution hotspots for targeted remediation. This work provides an example of how to use technologies for the purposes of environmental monitoring, and the importance of industry-specific pollution prevention planning to rehabilitate and enhance water quality for the benefit of future generations.
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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 | 0.000 | 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".