Sewer Pipe Condition Evaluation Using Ensemble Machine Learning

Aging infrastructure and rapid urbanization are intensifying the demands on sewer networks, which are critical to urban systems. Traditional, reactive monitoring methods lead to inefficiencies and higher risks of service disruptions. This study demonstrates the potential of an ensemble machine learning framework to improve sewer network condition monitoring by integrating multiple algorithms—including decision trees, KNN, logistic regression, and support vector machines—via ensemble techniques. Using historical inspection data and environmental factors, the model identifies key determinants of pipe condition to optimize maintenance planning. The proposed framework achieved 74.2% accuracy, 67.9% precision, 74.2% recall, and a 70.6% F1 score, outperforming individual models and effectively minimizing both false positives and negatives. This work underscores the transformative role of machine learning in sewer network management, setting the stage for smarter, more resilient urban infrastructure systems. Future efforts will focus on incorporating additional data sources and refining algorithms to further enhance monitoring accuracy and efficiency.