Failures in Water Transmission Lines (WTLs) can cause severe disruptions, high repair
costs, and extensive damage to surrounding areas. To mitigate these risks, there is a
growing interest in proactive management through predictive methods. Traditional
methods for identifying high-risk pipelines are often constrained by the scarcity ...
Failures in Water Transmission Lines (WTLs) can cause severe disruptions, high repair
costs, and extensive damage to surrounding areas. To mitigate these risks, there is a
growing interest in proactive management through predictive methods. Traditional
methods for identifying high-risk pipelines are often constrained by the scarcity of
available data, especially for large-diameter pipes, making accurate failure prediction
challenging. This study proposes a hybrid approach combining machine learning
techniques with hydraulic modelling to improve the accuracy of pipe failure risk
predictions. Using data from 48 water transmission networks in South Korea, including
pipe intrinsic properties, environmental conditions, operational factors, and failure
history from 2008 to 2023, the study applied tree-based machine learning models
(Random Forest, XGBoost, and CatBoost) along with various data sampling
techniques to predict the probability of pipe failure. XGBoost with class weighting
showed the best performance across key evaluation metrics, including F1-score, F2-
score, AUC-ROC and AUC-PR. A hydraulic model was used to assess the impact of
pipe isolation, quantifying water supply shortages and secondary damages. An
economic analysis was conducted to prioritize pipeline rehabilitation, balancing the
cost of repair with risk reduction. The results demonstrate a cost-effective approach to
risk-based maintenance planning, enabling utilities to allocate resources efficiently for
pipeline rehabilitation while minimizing disruption and water loss.
