Machine learning modeling for spectral transient-based leak detection

This paper describes Machine Learning (ML)-based framework to detect leaks in pipes using transient waves. The so-called Transient-Based Leak Detection (TBLD) technology is a non-convex multi-dimensional optimization problem, usually solved by inefficient Metaheuristic Optimization algorithms (MOAs). This paper proposes an efficient ML approach to address this drawback. At the core of this methodology, an ensemble of CatBoost models was trained on >3.8 million data records for classifying the leaky sections and predicting the leak sizes. Results showed that the ML models could detect leaks with 97% accuracy and an F1-score of 0.86, implying a significant superiority compared to the MOAs. Substituting the cumbersome optimizations with an ML-based approach, this paper opens a new line of research in the TBLD, which seems more welcome for complex pipe networks.