Enhancing accuracy and efficiency in calibration of drinking water distribution networks through evolutionary artificial neural networks and expert systems
Bibliographic record
Abstract
ABSTRACT The calibration of drinking water distribution network (DWDN) models is essential to ensure accurate simulation, efficient operation, and informed decision-making. As DWDNs evolve due to seasonal changes, shifting demands, or infrastructure updates, maintaining model accuracy over time becomes increasingly important. However, limited measurement availability and high model complexity make calibration a persistent challenge. To address this, ES-NEAT is introduced, an automatic calibration methodology that combines expert systems (ES) with neuro-evolution of augmenting topologies (NEAT). The method integrates expert knowledge with neural network evolution to efficiently solve high-dimensional calibration problems. ES-NEAT achieves high accuracy under sparse data conditions while keeping computational costs moderate. It also stores calibration knowledge in a structured format, enabling faster and more consistent recalibration over time. This adaptability supports long-term model reliability. The methodology was validated on a benchmark network and a real DWDN in Flanders, Belgium, demonstrating robust performance, efficient convergence, and generalizability across calibration scenarios.
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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 | 0.001 |
| 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".