Application of Machine Learning Algorithms to the Prediction of Water Main Deterioration
Bibliographic record
Abstract
Drinking water networks are among the essential infrastructure in cities worldwide. The failure of water mains jeopardizes this essential service and the safety of water users. However, across North America, the failure rate of older water mains has been increasing. The goal of this study is to compare the accuracy and applicability of machine learning algorithms to predict water main deterioration across Canadian water systems. In previous studies, different approaches were applied to only one or a few utilities. Nevertheless, it is valuable to compare results among various networks with different characteristics and levels of data collection. Accordingly, data was collected from thirteen Canadian water utilities, including Barrie, Calgary, Halifax, Kitchener, Markham, Region of Durham, Region of Waterloo, Saskatoon, St. John’s, Waterloo, Winnipeg, Victoria, and Vancouver. A variety of factors, including intrinsic, environmental, and operational, were used to develop more reliable predictions and assess the relative importance of each factor. Random forest (RF), artificial neural networks (ANN), extreme gradient boosting (XGBOOST), and logistic regression (LR) were applied to predict the probability of failure. Furthermore, RF, ANN, XGBOOST, and ElasticNet regression models were employed to predict age at first failure and the current rate of failures. Results indicated the superiority of XGBOOST over other models in predicting the probability of failure and the current rate of failure. However, for age at first failure, RF performed better. When datasets were significantly imbalanced, the application of the Synthetic Minority Oversampling Technique (SMOTE) provided more accurate predictions. Because these models provide predictions for every pipe in the network, they can be mapped to facilitate the visualization of deterioration. While models created for one utility cannot be accurately applied to other utilities, the same machine learning algorithms can be quickly and effectively adapted to multiple utilities. Overall, these models support robust and data-driven asset management decision-making.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".