Ensemble-based Machine Learning Approach for Improved Leak Detection in Water

This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem was formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A learning methodology was employed using several detection features extracted from acoustic signals, such as power spectral density and time-series data. The proposed multi-strategy ensemble learning (MEL) approach demonstrates a significant improvement in performance, resulting in a reduction of false positives reports by an order of magnitude.

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