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Water supply pipeline leakage is a major issue around the world. Leak detection and remediation can prevent water scarcity and some other problems. As a result, investigating pipeline leak-detecting technology has a high practical value. This study employs a promising technique for detecting pipeline leaks using Acoustic Emission (AE) signals. A dytran acceleration sensor was used to collect leakage signals in the time domain. The time-domain signal is transformed into a frequency domain by employing Fast Fourier Transform (FFT). The produced frequency signal has many dimensions which can be reduced to 17 by Principal Component Analysis (PCA). Intelligent leakage diagnosis techniques should eliminate time, and human intervention, and increase effectiveness. Machine learning (ML) models come into play at this point. To detect leakage, the hybrid ML model is proposed and it is compared with the conventional ML models. The best model for detecting water leakage is identified using the classification metrics.

Acoustic signal based water leakage detection system using hybrid machine learning model