How Cost Effective Is Machine Learning/AI Applied to Leak Detection and Pipe Replacement Prioritization?

Artificial intelligence (AI) and machine learning (ML) offer a cost-effective way of evaluating the condition of buried water mains taking in hundreds of variables which is more than normal management consulting practices. Machine learning as a cloud-based solution is able to leverage existing water main pipe data and produce a more accurate model than typical age-based models. Detecting more high-risk water main pipes using ML likelihood of failure (LoF) probability analysis provides improved decision making for directing leak detection, direct pipe inspection, and renewal and replacement activities. This paper explores the applications of AI/ML in underground water pipe distribution systems using example data sets from for a large water utility and a medium sized water utility analyzing the accuracy of an aged-based methodology versus applied ML.