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Abstract: With the acceleration of urbanization, the safety and reliability of water supply pipeline systems are becoming more and more important. Pipeline leakage not only leads to the waste of water resources, but also may cause a series of problems such as ground collapse and environmental pollution. The purpose of this paper is to explore the multiple leakage signal detection method of water supply pipeline based on machine learning. Through the acquisition and analysis of pipeline leakage signals, combined with machine learning algorithms, the accurate identification of different types of leakage signals is realized. In the study, we first collect the vibration and other signals generated when the pipeline leaks, and then preprocess these signals, including data amplification, feature extraction, and other steps. Then, we selected a variety of machine learning algorithms, such as Support Vector Machine (SVM), Dragonfly Algorithm-Random Forest (DA-RF), and Long Short-Term Memory Network (LSTM), to classify and recognize the preprocessed signals. The experimental results show that the adopted DA-RF model can effectively detect multiple leakage signals in water supply pipes with high accuracy. This study provides a new technical means for leakage detection in water supply pipes, which has a broad application prospect.
