Prediction of Water Demands in a Water Treatment Plant Using an Artificial Neural Network Model
Bibliographic record
Abstract
To provide improvements in efficiency and the ability to respond to changing external conditions, an artificial neural network (ANN) model is used to characterize the contents of reservoir(s) and water tower(s) sufficient to meet water demands.Maintaining water levels and daily treatment quantities can be effective to reduce the formation of disinfection byproducts (DBPs).Predictive models are developed to investigate the effects of maximum daily temperatures, incoming solar radiation and total daily precipitation (which influences water demands) and DBP formation at the water treatment plant.ANNs are semi-parametric regression estimators, and are well suited for predicting water demands and water quality as they can approximate virtually any function, to varying degrees of accuracy.In this application, predictions of water demand and DBPs are obtained using a simple backpropagation neural network.A comparative evaluation of the stepwise regression method and the ANN model are provided.The results show that the ANN obtains better accuracy than the regression method: it produced R 2 = 0.84 as compared to R 2 = 0.71 obtained by standard regression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".