A methodology for integrating time-lagged rainfall and river flow data into machine learning models to improve prediction of quality parameters of raw water supplying a treatment plant
Bibliographic record
Abstract
Abstract Rainfall and increased river flow can deteriorate raw water (RW) quality parameters such as turbidity and UV absorbance at 254 nm. This study aims to develop a methodology for integrating both time-lagged watershed rainfall and river flow data into machine learning models of the quality of RW supplying a drinking water treatment plant (DWTP). Spearman's rank non-parametric cross-correlation analyses were performed using both river flow and rain in the watershed and RW data from the water intake. Then, RW turbidity and RW UV254 were modelled, using a support vector regression (SVR) and an artificial neural network (ANN) under several prediction scenarios with time-lagged variables. River flow presented a very strong correlation with RW quality, whereas rainfall showed a moderate correlation. Time lags with maximum correlations between flow data and turbidity were a few hours, while for UV254, they were between 2 and 4 days, demonstrating varied time lags and a complex behaviour. The best performing scenario was the one that used time-lagged watershed rainfall and river flow as input data. The ANN performed better for both turbidity and UV254 than SVR. Results from this study suggest the possibility for new modelling strategies and more accurate chemical dosing for the removal of key contaminants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".