Ensemble-based machine learning approach for improved leak detection in water mains

Abstract

This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem is formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A supervised learning methodology has been employed using several detection features extracted from acoustic signals, such as power spectral density and time-series data. The training and validation data sets have been collected over several months from multiple cities across North America. The proposed solution includes a multi-strategy ensemble learning (MEL) using a gradient boosting tree (GBT) classification model, which has performed better in maximizing detection rate and minimizing false positives as compared with other classification models such as KNN, ANN, and rule-based techniques. Further improvements have been achieved using a multitude of GBT classifiers combined in a parallel ensemble method called bagging algorithm. The proposed MEL approach demonstrates a significant improvement in performance, resulting in a reduction of false positives reports by an order of magnitude. HIGHLIGHTS State-of-the-art machine learning (ML) algorithms are used for solving the leak detection problem in water mains.; A large number of acoustic signals and data are collected and used along with dimensionality reduction techniques as input features to ML algorithms.; A novel multi-strategy ensemble-based algorithm is applied to improve further the performance of the investigated leak detection classification problem.;

Keywords

• acoustic features
• leak detection
• machine learning
• multi-strategy ensemble learning