Enhancing Water Demand Forecasting: Leveraging LSTM Networks for Accurate Predictions
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to create a reliable water-demand forecasting system using Long Short-Term Memory networks. The model integrates hourly water demands from 10 District Metered Areas of a Water Distribution Network in northeast Italy and weather data, handling missing values with LSTM-based data imputation. It considers temporal aspects like time, weekdays, holidays, and weekend patterns, employing sine and cosine transformations to capture daily cycles. To ensure the model’s robustness, the testing was conducted during the last week of the dataset, specifically week 81, with iterative adjustments to the LSTM’s hyperparameters to optimize prediction accuracy. These tuning efforts focused on learning rate, number of layers, and batch size, tailored to maximize the system’s performance. This method is essential for smart decision-making in water utility management and demonstrates significant potential for improving operational efficiencies.
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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) | 0.002 | 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 it