Explore the vast range of titles available.

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.

This study explores the development and deployment of a predictive model for pipe failures within Barcelona's Water Distribution System. Using the XGBoost algorithm, refined through systematic analysis of explanatory variables, machine learning algorithms, and hyperparameter configurations, the model predicted up to 30.2% of expected pipe failures with a 5% annual renewal rate and 10.32% with a 1% renewal rate. Material-specific responses to predictive variations were observed, with ductile iron and HDPE pipes showing significantly different behaviours compared to non-cylinder reinforced concrete and fibre-cement pipes. The model development process was heavily guided by domain expertise, as reflected in the custom-built dataset, which was meticulously created considering local system conditions, strategic hyperparameter tuning, and optimisation using business-focused metrics. A preliminary exploration employing SHAP (Shapley Additive exPlanations) assessed the importance of local explanatory variables, which varied across pipe materials. This research advances understanding of how specific materials within water systems respond to predictive modelling of pipe failures, emphasising the vital role of extensive historical data in enhancing predictive accuracy and informing infrastructure planning and management.