Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems

Pipe failures in water distribution infrastructure (WDI) have significant economic, environmental and public health impacts. To alleviate these impacts, repair and replacement decisions need to be prioritized to effectively reduce failure rates. In this study, a computational framework is proposed for WDI asset management that couples spatial clustering analysis with predictive modeling of pipe failures. First, hotspot/coldspot clusters of statistically significant high/low failure rates are identified using local indicators of spatial association. Second, the predictive abilities of eight statistical learning techniques are systematically tested, and the best-performing method is implemented to forecast failure rates,(breaks/(km.year)) within different sectors of the WDI. Third, the framework is implemented to compare the impact of adopting proactive instead of reactive pipe replacement strategies. Applying the framework to a real-life, large-scale WDI revealed that spatial clustering of pipe failures improves the accuracy of the prediction models.

Automated summary

This study proposes a computational framework for water distribution infrastructure asset management that combines spatial clustering analysis with predictive modeling of pipe failures. The results show that spatial clustering improves the accuracy of the prediction models.