Risk-Based Prioritization of Water Main Replacements under Climate Change Scenarios
Bibliographic record
Abstract
Ensuring the resilience of water distribution networks is essential for sustainable urban development, particularly in the face of climate change and aging infrastructure.However, many parts of these systems in North America have exceeded their expected service life, leading to an increasing frequency of failures.This study presents a comprehensive risk-based framework for prioritizing water main replacements by integrating machine learning-based probability of failure (PoF) assessments with consequence of failure (CoF) evaluations.The methodology leverages historical failure records, environmental conditions, and climate projections under three Shared Socioeconomic Pathway (SSP) scenarios.The City of Waterloo, Ontario, serves as a case study, providing real-world validation of the proposed approach.To estimate PoF, four machine learning models-Random Forest, k-Nearest Neighbors, Artificial Neural Networks, and Light Gradient Boosting Machine-were tested, with LightGBM demonstrating the highest F1-score.The CoF assessment incorporates economic, social, and environmental dimensions, ensuring a holistic evaluation of failure consequences.Results indicate that climate change significantly influences failure risk, with extreme temperature fluctuations accelerating pipeline deterioration.Warmer climate scenarios (SSP2 and SSP5) lead to a greater proportion of high-risk water mains, necessitating proactive infrastructure management strategies.By integrating advanced predictive modeling with a comprehensive risk assessment approach, this study provides municipalities with a data-driven decision-making tool for infrastructure renewal.The findings highlight the importance of climate-adaptive planning to enhance the resilience and sustainability of water distribution networks amid evolving environmental challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".