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A Tree-Based Machine Learning Method for Pipeline Leakage Detection

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
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Water 2022, 14(18), 2833; https://doi.org/10.3390/w14182833
Received: 6 September 2022 / Revised: 8 September 2022 / Accepted: 9 September 2022 / Published: 12 September 2022
(This article belongs to the Section Urban Water Management)
Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited compared to studies on lab-scale leak detection. The on-site leak signals have stronger interference and randomness, while leak signals in the laboratory are relatively simpler. To better assist on-site leak detection operations, the present paper develops and compares three ML-based models. For this purpose, many on-site tests were carried out, and tens of thousands of sets of on-site leak detection signals were collected. More than 6000 sets of these signals were marked and the signal features were extracted and analyzed from a statistical point of view. It was found that features such as the main frequency, the spectral roll-off rate, the spectral flatness, and one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish the leakage signals from non-leakage signals. After training the decision tree model, the performances of the random forest and Adaboost models were thoroughly compared. It was found that the false positive rates of the three models were 9.80%, 8.27% and 7.35%, all lower than 10%. In particular, the Adaboost model had the lowest false positive rate of 7.35%. The recall rate of the random forest and Adaboost models were 100% and 99.52%. View Full-Text
Keywords: water distribution system; leak detection; machine learning; Adaboost model; random forest model water distribution system; leak detection; machine learning; Adaboost model; random forest model
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MDPI and ACS Style

Shen, Y.; Cheng, W. A Tree-Based Machine Learning Method for Pipeline Leakage Detection. Water 2022, 14, 2833. https://doi.org/10.3390/w14182833

AMA Style

Shen Y, Cheng W. A Tree-Based Machine Learning Method for Pipeline Leakage Detection. Water. 2022; 14(18):2833. https://doi.org/10.3390/w14182833

Chicago/Turabian Style

Shen, Yongxin, and Weiping Cheng. 2022. "A Tree-Based Machine Learning Method for Pipeline Leakage Detection" Water 14, no. 18: 2833. https://doi.org/10.3390/w14182833

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