Identification of the number of leaks in water supply pipes based on wavelet scattering network and Bi-LSTM model with Bayesian optimization

Abstract

Water supply pipeline leaks have significant impacts on urban areas and daily life. Acoustic techniques are widely applied in its leak detection, but there is a lack of research on quantifying multiple leaks in pipelines. This study presents a novel approach using a Wavelet Scattering Network (WSN) and Bi-directional Long and Short-Term Memory (Bi-LSTM) model with Bayesian optimization to identify leak quantities accurately. The WSN autonomously extracts signal features, which are subsequently processed by the Bi-LSTM for classification. Through Bayesian optimization, the model’s hyperparameters are refined to enhance performance. The results exhibit an impressive 90.8 % classification accuracy, showcasing the model’s effectiveness in accurately identifying leak quantities. In comparison with existing models, the proposed method stands out for its superior recognition accuracy, shorter training time, and enhanced noise immunity. Ablation experiments further confirm the distinct roles and contributions of the WSN, Bayesian optimization, and Bi-LSTM components in achieving these exceptional results.