Leak Detection and Localization in Water Networks Using Convolutional Neural Networks with a Modified Loss Function
Bibliographic record
Abstract
Leaks in water distribution networks can cause considerable water wastage, infrastructure damage, and contamination. Various solutions have been developed for leak detection. Previous researchers have applied deep learning, utilizing traditional loss functions, such as F1 score or accuracy. However, these approaches may oversimplify the real challenge of quickly repairing leaks. This study aims to develop a Convolutional Neural Network (CNN) for leak detection, trained with the specific objective of optimizing leak detection. A synthetic data set is developed by simulating leaks using EPANET in Python. The leakage detection model was trained with a modified loss function based on the distance to the actual leak. Results show that the use of the modified loss function led to better leak detection accuracy than with the traditional loss function. These findings provide insights into strategies for using novel deep-learning models to efficiently detect leaks.
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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 | 0.000 |
| 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 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".