Water Quality Modeling of the River Ganga in the Northern Region of India Using the Artificial Neural Network Technique
Bibliographic record
Abstract
Water quality modeling with dynamic parameters, especially of rivers, is important in terms of proactive pollution management strategies. Techniques such as artificial neural networks (ANNs) have become popular for such applications. In the present study, an ANN is used to construct a multilayer perceptron and radial basis function neural network model to simulate and predict dissolved oxygen in the River Ganga in selected regions of Uttar Pradesh, and to demonstrate its application in identifying complex nonlinear relationships between input and output variables. The results of the model analysis demonstrate that the multi-layer perceptron model provides greater correlation coefficients (R = 0.993) and a lower mean square error (RMSE = 0.1984) than the radial basis function model (R = 0.789; RMSE = 1.0011). The results of the analysis suggest the suitability of the proposed MLP-ANN model to predict water quality parameters such as dissolved oxygen using limiting data sets for the River Ganga, in particular, and other rivers in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".