Identifying risk factors associated with the pipeline failure using machine learning methods

Abstract
Pipelines are critical infrastructure for water distribution, and their failures can lead to significant economic losses, service disruptions, and public safety risks. Understanding the key drivers of pipe failure is essential for proactive maintenance and sustainable urban planning. In this study, we employ Neural Networks and Random Forest to investigate the impact of geographical and meteorological factors on pipeline failure. Our analysis reveals that meteorological conditions are the dominant predictors of pipe failure. Specifically, snow on ground, total snow, and total rain emerge as the most important variables, with snow on ground consistently identified as the single most influential factor across both models. In contrast, geographical factors contribute only minimally to predictive performance. These findings suggest that weather-related variables, particularly snow accumulation, should be prioritized in predictive maintenance frameworks for water pipelines. The study demonstrates the effectiveness of machine learning approaches in uncovering actionable patterns in infrastructure data and provides practical insights for utility companies aiming to reduce failure rates and optimize resource allocation.