This paper conducts a comparative study on leak detection techniques using vibration sensor data installed in water pipeline networks. By applying machine learning techniques, we have implemented a technology to detect and classify leaks in water pipelines, thereby enabling the prediction of leaks in the water mains. The vibration sensor dataset used in this study consists of leak detection sensor data installed in water supply meters and valve chambers within the water supply network. This dataset includes data provided by the public data portal (AI hub) as well as investigation data provided by a company. The investigation data were collected in the field by leakage detection experts. We designed leak classification models using various machine learning techniques and measured and analyzed the performance of each dataset using feature importance.
KSP Keywords
Classification models, Feature Importance, Machine Learning technique(MLT), Pipeline networks, Public Data, Vibration sensor, Water mains, Water pipelines, Water supply network, comparative study, data portal
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