Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models †

Abstract

The state of watermain systems is intrinsically linked to climate factors such as fluctuations in temperature and variations in rainfall. However, the integration of these climate-related factors into watermain failure prediction models, with a specific focus on climate change impacts, remains insufficiently explored. In response to these challenges, this research incorporates the potential effects of climate change on the frequency of watermain breaks by utilizing machine learning techniques, including K-Nearest Neighbours, Random Forest, Artificial Neural Network, and Extreme Gradient Boosting. By leveraging projected climate trends, the models provide actionable intelligence that can inform the development of more robust maintenance and rehabilitation strategies.