Determining Water Pipe Leakage Using an RP-CNN Model to Identify the Causes and Improve Poor-Accuracy Cases

Abstract

This study aimed to assess and improve the accuracy of a water leakage detection model proposed in preliminary research. The poor results for water leakage sound (recall) and background noise (specificity) were clarified using countermeasures in accordance with each condition. Additionally, frequency amplification in the range of 500–600 Hz, the attenuation of weak components, and a band-stop filter were used to remove the 50 Hz component and harmonics. Pre-processing was carried out in the form of amplification, with weak noise removed using a band-stop filter. The results showed that the application of the proposed model improved the detection accuracy by 80% at the observation points that initially had poor accuracy. Thus, the proposed method was effective at improving the performance of the Recurrence Plot-Convolutional Neural Network (RP-CNN) model for detecting water leakages.

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