Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Methodology
2.2. Data Generation
2.3. Use of Machine Learning Methods
2.4. Input and Output Parameters
3. Results
3.1. Supervised Methods
3.2. Unsupervised Methods
3.3. Artificial Neural Network
3.4. Analysis of the Water Leak in the Scientific Campus of Lille University
3.5. Analysis of the Daily Water Consumption (Qd)
3.6. Leakage Analysis
3.7. Leakage Localization
4. Discussion
- Three supervised methods: logistic regression, decision tree, and random forest;
- Two unsupervised methods: The hierarchical classification method and a combination of the PCA and K-means classification method;
- The ANN
- Excellent performance of the supervised methods in the localization of leaks in the water network. Both the logistic regression and the random forest predicted the position of the leak with an accuracy = 1.0. In contrast, the decision tree predicted leaks with an accuracy = 0.98 with pressure and flow data;
- Excellent performances by the ANN for the localization of water leaks in the water network (accuracy = 1.0);
- Some difficulties in exploiting the clustering capacity of the unsupervised methods in the leak localization because of overlapping clusters.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Actual | Prediction | ||
Positive | Negative | ||
Positive | True Positive | False Negative | |
Negative | False Positive | True Negative |
Zone | Position of Water Leak |
---|---|
1 (62 leak scenarios) | L1, L2, L4, L7, L8, L10, L13, L18, L20 L10 + L11; L10 + L12; L10 + L13; L10 + L18; L10 + L2; L10 + L20; L10 + L3; L10 + L4; L10 + L7; L10 + L8; L11 + L12; L11 + L13; L11 + L18; L11 + L2; L11 + L20; L11 + L3: L11 + L4; L11 + L7; L11 + L8; L12 + L13; L12 + L18; L12 + L2; L12 + L20; L12 + L4; L12 + L7; L12 + L8; L13 + L18; L13 + L2; L13 + L20; L13 + L3; L13 + L4; L13 + L7; L13 + L8; L18 + L2; L18 + L20; L18 + L3; L18 + L4; L18 + L7; L18 + L8; L2 + L20; L2 + L4; L2 + L7; L2+ L8; L1 + L10; L1 + L11; L1 + L12; L1 + L13; L1 + L18; L1 + L2; L1 + L20; L1 + L3; L1 + L4; L1 + L7; L1 + L8 |
2 (35 leak scenarios) | L5, L6, L9, L22, L23, L24, L25, L30 L23 + L22; L24 + L22; L24 + L23; L25 + L22; L25 + L23; L25 + L24; L30 + L22; L30 + L23; L30 + L24; L30 + L25; L5 + L22; L5 + L23; L5 + L24; L5 + L25; L5 + L30; L6 + L22; L6 + L24; L6 + L25; L6 + L30; L6 + L5; L9 + L22; L9 + L23; L9 + L24; L9 + L25; L9 + L30; L9 + L5; L9 + L6: |
3 (30 leak scenarios) | L41, L42, L43, L44, L46, L47, L48, L49, L50 L41 + L44; L41 + L46; L41 + L47; L41 + L48; L41 + L50; L42 + L44; L42 + L46; L42 + L47; L42 + L48; L42 + L50; L43 + L44; L43 + L46; L43 + L47; L43 + L48; L43 + L50; L44 + L46; L44 + L47; L44 + L48; L44 + L50; L46 + L47; L47 + L48; L47 + L50; L48 + L49; L50 + L49 |
4 (47 leak scenarios) | L31, L32, L33, L34, L35, L36, L37, L38, L39, L40, L45 L31 + L36; L31 + L37; L31 + L38; L31 + L39; L31 + L45; L32 + L36; L32 + L37; L32 + L38; L32 + L39; L32 + L45; L33 + L36; L33 + L37; L33 + L38; L33 + L39; L33 + L45; L34 + L36; L34 + L37; L34 + L38; L34 + L39; L34 + L45; L35 + L36; L35 + L37; L35 + L38; L35 + L39; L35 + L45; L36 + L37; L36 + L38; L36 + L39L36 + L45;L37 + L38; L37 + L39; L37 + L45; L38 + L39; L38 + L45; L39 + L45; L40 + L45 |
5 (41 leak scenarios) | L14, L16, L17, L19, L21, L26, L27, L28, L29 L14 + L21; L14 + L26; L14 + L27; L14 + L28; L14 + L29; L15 + L21; L15 + L26; L15 + L27; L15 + L28; L15 + L29; L16 + L21; L16 + L26; L16 + L27; L16 + L28; L16 + L29; L17 + L21; L17 + L26; L17 + L27; L17 + L28; L17 + L29; L19 + L21; L19 + L26; L19 + L27; L19 + L28; L19 + L29; L21 + L26; L21 + L27; L21 + L28; L21 + L29;L26 + L27; L26 + L28; L26 + L29; L27 + L28; L27 + L29; L28 + L29 |
Zone | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Pressure node | PZ1 | PZ2 | PZ3 | PZ4 | PZ5 |
Minimum | Maximum | Average | Standard Deviation | |
---|---|---|---|---|
FL1 (%) | 0.13 | 0.71 | 0.4 | 0.15 |
FL2 (%) | 0.23 | 0.62 | 0.35 | 0.82 |
FL3 (%) | 0.60 | 0.59 | 0.23 | 0.11 |
PZ1 (m) | 2.0 | 39.7 | 28.4 | 9.4 |
PZ2 (m) | 1.4 | 39.4 | 27.4 | 9.3 |
PZ3 (m) | 11.0 | 39.8 | 35.6 | 4.5 |
PZ4 (m) | 1.8 | 39.2 | 29.2 | 10.2 |
PZ5 (m) | 4.9 | 39.4 | 30.0 | 7.4 |
Leak Zone | FL1 | FL2 | FL3 |
---|---|---|---|
1 | Strong | Medium | Low |
2 | Medium | Medium | Low to medium |
3 | Low | Medium | Strong |
4 | Low | Strong | Low to medium |
5 | Medium | Medium | Low to medium |
Method | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Logistic Regression | 1.0 | 1.0 | 1.0 | 1.0 |
Decision Tree | 0.95 | 0.96 | 0.95 | 0.95 |
Random Forest | 1.0 | 1.0 | 1.0 | 1.0 |
Method | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Logistic Regression | 1.0 | 1.0 | 1.0 | 1.0 |
Decision Tree | 0.88 | 0.91 | 0.94 | 0.91 |
Random Forest | 1.0 | 1.0 | 1.0 | 1.0 |
Method | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Decision Tree | 0.98 | 0.97 | 0.97 | 0.96 |
Data | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Flow data | 1.0 | 1.0 | 1.0 | 1.0 |
Pressure data | 1.0 | 1.0 | 1.0 | 1.0 |
Flow and pressure data | 1.0 | 1.0 | 1.0 | 1.0 |
F1D m3/Day | F2D m3/Day | F3D m3/Day | Total (Qd) m3/Day | |
---|---|---|---|---|
Minimum | 100 | 197 | 51 | 414 |
Maximum | 772 | 462 | 454 | 1680 |
Average | 442 | 251 | 197 | 890 |
Standard deviation | 143 | 33 | 59 | 219 |
Day | Group | Qd (m3/Day) | Qd-Average (m3/Day) |
---|---|---|---|
76 | G1 (76) | 1354 | 464 |
86 | G2 (86) | 1216 | 326 |
260 | G3 (260. 261) | 1280 | 390 |
261 | G3 (260. 261) | 1357 | 467 |
264 | G4 (264–2675) | 1383 | 493 |
265 | G4 (264–2675) | 1463 | 573 |
266 | G4 (264–2675) | 1477 | 587 |
267 | G4 (264–2675) | 1404 | 514 |
268 | G4 (264–2675) | 1396 | 506 |
269 | G4 (264–2675) | 1217 | 327 |
270 | G4 (264–2675) | 1201 | 311 |
271 | G4 (264–2675) | 1435 | 545 |
272 | G4 (264–2675) | 1459 | 569 |
273 | G4 (264–2675) | 1455 | 565 |
274 | G4 (264–2675) | 1624 | 734 |
275 | G4 (264–2675) | 1680 | 790 |
327 | G5 (327) | 1208 | 318 |
Day | Groupe | FL3 (%) | FL2 (%) | FL1 (%) |
---|---|---|---|---|
76 | G1 (76) | 20 | 23 | 57 |
86 | G2 (86) | 21 | 22 | 57 |
260 | G3 (260, 261) | 26 | 26 | 48 |
261 | G3 (260, 261) | 26 | 26 | 48 |
264 | G4 (264–2675) | 28 | 26 | 46 |
265 | G4 (264–2675) | 27 | 26 | 47 |
266 | G4 (264–2675) | 28 | 27 | 45 |
267 | G4 (264–2675) | 29 | 27 | 44 |
268 | G4 (264–2675) | 28 | 27 | 44 |
269 | G4 (264–2675) | 28 | 29 | 42 |
270 | G4 (264–2675) | 29 | 30 | 41 |
271 | G4 (264–2675) | 28 | 27 | 46 |
272 | G4 (264–2675) | 28 | 26 | 46 |
273 | G4 (264–2675) | 28 | 26 | 46 |
274 | G4 (264–2675) | 27 | 26 | 47 |
275 | G4 (264–2675) | 27 | 27 | 45 |
327 | G5 (327) | 22 | 23 | 55 |
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Mashhadi, N.; Shahrour, I.; Attoue, N.; El Khattabi, J.; Aljer, A. Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems. Smart Cities 2021, 4, 1293-1315. https://doi.org/10.3390/smartcities4040069
Mashhadi N, Shahrour I, Attoue N, El Khattabi J, Aljer A. Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems. Smart Cities. 2021; 4(4):1293-1315. https://doi.org/10.3390/smartcities4040069
Chicago/Turabian StyleMashhadi, Neda, Isam Shahrour, Nivine Attoue, Jamal El Khattabi, and Ammar Aljer. 2021. "Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems" Smart Cities 4, no. 4: 1293-1315. https://doi.org/10.3390/smartcities4040069
APA StyleMashhadi, N., Shahrour, I., Attoue, N., El Khattabi, J., & Aljer, A. (2021). Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems. Smart Cities, 4(4), 1293-1315. https://doi.org/10.3390/smartcities4040069