A Tree-Based Machine Learning Method for Pipeline Leakage Detection
Abstract
:1. Introduction
2. Methodology
2.1. Data Collection
2.2. Feature Set
2.2.1. Dominant Frequency
2.2.2. Spectral Flatness
2.2.3. Spectral Roll-Off Rate
2.2.4. One-D MFCC
2.3. ML Models
2.3.1. Data Balance
2.3.2. Decision Tree
3. Performance of the ML Models
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Name | Meaning |
---|---|
TN | Non-leakage both in reality and in prediction |
FP | Non-leakage in reality but leakage in prediction |
FN | Leakage in reality but non-leakage in prediction |
TP | Leakage both in reality and in prediction |
Precision | |
Training accuracy | in training |
Validation accuracy | in validation |
Recall rate | |
F1_score | |
False Positive Rate |
Training Accuracy | Validation Accuracy | Precision | Recall Rate | False Positive Rate | F1_Score | |
---|---|---|---|---|---|---|
Decision Tree | 99.96% | 90.91% | 90.20% | 94.69% | 9.80% | 0.9239 |
Random Forest | 99.82% | 95.27% | 92.01% | 100% | 8.27% | 0.9584 |
Adaboost | 99.96% | 95.80% | 92.80% | 99.52% | 7.35% | 0.9604 |
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Shen, Y.; Cheng, W. A Tree-Based Machine Learning Method for Pipeline Leakage Detection. Water 2022, 14, 2833. https://doi.org/10.3390/w14182833
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 StyleShen, 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
APA StyleShen, Y., & Cheng, W. (2022). A Tree-Based Machine Learning Method for Pipeline Leakage Detection. Water, 14(18), 2833. https://doi.org/10.3390/w14182833