Important Factors For Water Main Break Prediction Across 13 Canadian Systems
Notice bibliographique
Résumé
Water main breaks can jeopardize the safe delivery of clean water and incur significant costs. To mitigate these risks, water main breaks have been predicted through physical and statistical approaches. The latter are less complex and can provide satisfactory results with less data. While many factors can contribute to breaks, the factors applied in previous studies depended on local data availability. Because other studies have focused on a few systems at a time, a broad comparison of factor importance has not been possible. This limits the understanding of the impact of different factors on water main deterioration. The present study identifies the most important factors driving water main breaks across 13 Canadian water systems. Twenty-eight factors describing physical, historical, protection, environmental and operational attributes were compiled and cleaned. Availability of each attribute differed by system. To evaluate the importance of both numerical and categorical attributes together, two approaches were tested, categorical principal component analysis (CATPCA) and recursive feature elimination with cross-validation (RFECV). The target variable in both cases was set as yearly break status, either broken or non-broken. While CATPCA provides the contribution of each attribute to the target, RFECV provides a tuned predictive model with selected attributes. The RFECV approach was applied with Random Forest and XGBoost models, both types of machine learning models which have been shown to produce accurate results in water main break prediction. Results from both approaches showed that physical and historical attributes are generally important across all systems. Other types of data, i.e. protection and operational are less available. When protection data is available it was shown to be even more important than physical and historical attributes. Specifically, with CATPCA, lining age and lining material were found to have a higher contribution to break status than pipe age and lining status. With RFECV lining age and lining material were also included in the best models, in particular for systems with greater percentage of lined pipes. These results indicate the choice and timing of lining are key in extending the service life of water mains. Furthermore, this data should be collected if protection practices are in place, to more accurately predict deterioration and future costs. The results also point to an opportunity to collect more operational data. Among attributes collected by only one utility, pipe pressure, roughness, and dead-end, were found to be important in CATPCA and RFECV. Thus, pipe dissipation and water stagnation could lead to greater pipe deterioration. Further studies are required to quantify the impacts of different pressure ranges and network designs on deterioration.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».