A Tree-Based Machine Learning Method for Pipeline Leakage Detection

نویسندگان

چکیده

Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak operations water distribution systems (WDSs). However, studies leakage on-site signals are limited compared to lab-scale detection. The have stronger interference and randomness, while the laboratory relatively simpler. To better operations, present paper develops compares three ML-based models. For this purpose, many tests were carried out, tens of thousands sets collected. More than 6000 these marked signal features extracted analyzed from a statistical point view. It was found that such as main frequency, spectral roll-off rate, flatness, one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish non-leakage signals. After training decision tree model, performances random forest Adaboost thoroughly compared. false positive rates 9.80%, 8.27% 7.35%, all lower 10%. In particular, model had lowest rate 7.35%. recall 100% 99.52%.

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ژورنال

عنوان ژورنال: Water

سال: 2022

ISSN: ['2073-4441']

DOI: https://doi.org/10.3390/w14182833