A review of leak detection and prediction methods in water distribution systems using machine learning
La revisión bibliográfica se realiza teniendo en cuenta articulos científicos publicados en los últimos 5 años que utilicen machine learning para detectar fugas en sistemas de distribución de agua potable.
- Autores:
-
Acevedo Pérez, Ana Sofía
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/65925
- Acceso en línea:
- http://hdl.handle.net/1992/65925
- Palabra clave:
- Leak detection
Leak prediction
Machine learning
Water distribution systems
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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A review of leak detection and prediction methods in water distribution systems using machine learning |
| title |
A review of leak detection and prediction methods in water distribution systems using machine learning |
| spellingShingle |
A review of leak detection and prediction methods in water distribution systems using machine learning Leak detection Leak prediction Machine learning Water distribution systems Ingeniería |
| title_short |
A review of leak detection and prediction methods in water distribution systems using machine learning |
| title_full |
A review of leak detection and prediction methods in water distribution systems using machine learning |
| title_fullStr |
A review of leak detection and prediction methods in water distribution systems using machine learning |
| title_full_unstemmed |
A review of leak detection and prediction methods in water distribution systems using machine learning |
| title_sort |
A review of leak detection and prediction methods in water distribution systems using machine learning |
| dc.creator.fl_str_mv |
Acevedo Pérez, Ana Sofía |
| dc.contributor.advisor.none.fl_str_mv |
Saldarriaga Valderrama, Juan Guillermo |
| dc.contributor.author.none.fl_str_mv |
Acevedo Pérez, Ana Sofía |
| dc.subject.keyword.none.fl_str_mv |
Leak detection Leak prediction Machine learning Water distribution systems |
| topic |
Leak detection Leak prediction Machine learning Water distribution systems Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
La revisión bibliográfica se realiza teniendo en cuenta articulos científicos publicados en los últimos 5 años que utilicen machine learning para detectar fugas en sistemas de distribución de agua potable. |
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2023 |
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2023-03-27T20:39:01Z |
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2023-03-27T20:39:01Z |
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2023-03-27 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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eng |
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eng |
| dc.relation.references.es_CO.fl_str_mv |
Adanza Dopazo, D., Everson, R., Rogerson, S., Farmani, R., & Dragan, S. (2021). A Leakage Detection System with an Efficient Prioritization at a District Meter Area Level. World Environmental and Water Resources Congress 2021. doi:10.1061/9780784483466.095 Alizadeh, Z., Yazdi, S., Mohammadiun, K., Hewage, K., & Sadiq, R. (2019). Evaluation of data driven models for pipe burst prediction in urban water distribution systems. Urban Water Journal, 16(2), 136-145. doi:10.1080/1573062X.2019.1637004 American Society of Civil Engineers ASCE. (2022). Special Collection on Battle of the Leakage Detection and Isolation Methods (BattLeDIM). Water Resources Planning and Management. Obtenido de https://ascelibrary.org/jwrmd5/leakage_detection_isolation_methods Ayadi, A., Ghorbel, O., BenSalah, M., & Abid, M. (2019). Kernelized technique for outliers detection to monitoring water pipeline based on WSNs. Computer Networks, 150, 179¿189. doi:10.1016/j.comnet.2019.01.004 Ayati, A. H., Haghighi, A., & Ghafouri, H. R. (2022). Machine Learning¿Assisted Model for Leak Detection in Water Distribution Networks Using Hydraulic Transient Flows. Journal of Water Resources Planning and Management, 148(2). doi:10.1061/(ASCE)WR.1943-5452.0001508 Aymon, L., Decaix, J., Carrino, F., Mudry, P.-A., Mugellini, E., Khaled, O., & Baltensperger, R. (2019). Leak detection using Random Forest and pressure simulation. 2019 6th Swiss Conference on Data Science (SDS). doi:10.1109/SDS.2019.00008 Bohorquez, J., Alexander, B., Simpson, A. R., & Lambert, M. F. (2020). Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks. Journal of Water Resources Planning and Management, 6(146). doi:10.1061/(ASCE)WR.1943-5452.0001187 Bohorquez, J., Lambert, M. F., Alexander, B., Simpson, A. R., & Abbott, D. (2022). Stochastic Resonance Enhancement for Leak Detection in Pipelines Using Fluid Transients and Convolutional Neural Networks. Journal of Water Resources Planning and Management, 148(3). doi:10.1061/(ASCE)WR.1943-5452.0001504 Bohorquez, J., Simpson, A. R., Lambert, M. F., & Alexander, B. (2021). Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines. Journal of Water Resources Planning and Management, 147(1). doi:10.1061/(ASCE)WR.1943-5452.0001296. Chen, J., Feng, X., & Xiao, S. (2021). An iterative method for leakage zone identification in water distribution networks based on machine learning. Structural Health Monitoring, 20(4), 1938 ¿1956. doi:10.1177/1475921720950470 Fan, X., Wang, X., Zhang, X., & Yu, X. (2022). Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors. Reliability Engineering and System Safety, 219. doi:10.1016/j.ress.2021.108185 Fan, X., Zhang, X., & Yu, X. (2021). Machine learning model and strategy for fast and accurate detection of leaks in water supply network. Journal of Infrastructure Preservation and Resilience, 2(10). doi:10.1186/s43065-021-00021-6 Garðarsson, G. Ö., Boem, F., & Toni, L. (2022). Graph-Based Learning for Leak Detection and Localisation in Water Distribution Networks. IFAC Papers OnLine, 55(6), 661-666. doi:10.1016/j.ifacol.2022.07.203 Gonçalves Nascimento Gouveia, C., & Kepler Soares, A. (2021). Water Connection Bursting and Leaks Prediction Using Machine Learning. World Environmental and Water Resources Congress 2021. doi:10.1061/9780784483466.093 Guo, G., Yu, X., Liu, S., Ma, Z., Wu, Y., Xu, X., . . . Wu, X. (2021). Leakage Detection inWater Distribution Systems Based on Time-Frequency Convolutional Neural Network. Journal of Water Resources Planning and Management, 147(2). doi:10.1061/(ASCE)WR.1943-5452.0001317 Hu, X., Han, Y., Yu, B., Geng, Z., & Fan, J. (2021). Novel leakage detection and water loss management of urban water supply network using multiscale neural networks. Journal of Cleaner Production, 278. doi:10.1016/j.jclepro.2020.123611 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R. New York: Springer Science+Business Media. Javadiha, M., Blesa, J., Soldevila, A., & Vicenç Puig. (2019). Leak Localization in Water Distribution Networks using Deep Learning. 2019 6th International Conference on Control, Decision and Information Technologies. doi:10.1109/CoDIT.2019.8820627 Jian, C., Gao, J., & Xu, Y. (2022). Anomaly Detection and Classification in Water Distribution Networks Integrated with Hourly Nodal Water Demand Forecasting Models and Feature Extraction Technique. Journal of Water Resources Planning and Management, 148(11). doi:10.1061/(ASCE)WR.1943-5452.0001616 Kizilöz, B., Sisman, E., & Oruç, H. N. (2022). Predicting a water infrastructure leakage index via machine learning. Utilities Policy, 75. doi:10.1016/j.jup.2022.101357 Kuhn, M., & Johnson, K. (2016). Applied Predictive Modeling. New York: Springer Science+Business Media. Kumar, S., & Chong, I. (2018). Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States. International Journal of Environmental Research and Public Health, 15(12), 2907. doi:10.3390/ijerph15122907 Li, J., Zheng, W., & Lu, C. (2022). An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network. Water Resources Management, 36, 2309-2325. doi:10.1007/s11269-022-03144-x Li, Z., Wang, J., Yan, H., Li, S., Tao, T., & Xin, K. (2022). Fast Detection and Localization of Multiple Leaks in Water Distribution Network Jointly Driven by Simulation and Machine Learning. Journal of Water Resources Planning and Management, 148(9). doi:10.1061/(ASCE)WR.1943-5452.0001574 Liu, M., Yang, J., Li, S., Zhou, Z., Fan, E., & Zheng, W. (2022). Robust GMM least square twin K-class support vector machine for urban water pipe leak recognition. Expert Systems With Applications, 195. doi:10.1016/j.eswa.2022.116525 McClements, D. (25 de 05 de 2020). NewEngineer. Obtenido de https://newengineer.com/blog/the-best-quotes-about-engineering-1311086 Momeni, A., & Piratla, K. (2022). Prediction of Water Pipeline Condition Parameters Using Artificial Neural Networks. Pipelines 2022 21. doi:10.1061/9780784484289.003 Momeni, A., Piratla, K., & Madathil, K. C. (2022). Application of Neural Network¿Based Modeling for Leak Localization in Water Mains. Journal of Pipeline Systems Engineering and Practice, 13(4). doi:10.1061/(ASCE)PS.1949-1204.0000674 Muniz Do Nascimento, W., & Gomes-Jr, L. (2022). Enabling low-cost automatic water leakage detection: a semi-supervised, autoML-based approach. Urban Water Journal. doi:10.1080/1573062X.2022.2056710 Puust, R., Kapelan, Z., Savic, D. A., & Koppel, T. (2010). A review of methods for leakage management in pipe networks. Urban Water Journal, 7(1), 25-45. doi:10.1080/15730621003610878 Quiñones-Grueiro, M., Bernal-de-Lizarazo, J. M., Verde, C., Prieto-Moreno, A., & Llanes-Santiago, O. (2018). Comparisson of Classifiers for Leak Location in Water Distribution Networks. IFAC Papers Online, 51(24), 407-413. doi:10.1016/j.ifacol.2018.09.609 Ravichandran, T., Gavahi, K., Ponnambalam, K., Burtea, V., & Mousavi, S. (2021). Ensemble-based machine learning approach for improved leak detection in water mains. Journal of Hydroinformatics, 23(2), 301-322. doi:10.2166/hydro.2021.093 Romano, M., Kapelan, Z., & Savic, D. A. (2014). Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems. Journal of Water Resources Planning and Management, 140(4), 407-557. doi:10.1061/(ASCE)WR.1943-5452.0000339 Romero, L., Blesa, J., Vicenç, P., Cembrano, G., & Trapiello, C. (2020). First Results in Leak Localization in Water Distribution Networks using Graph-Based Clustering and Deep Learning. IFAC PapersOnLine, 53(2). doi:10.1016/j.ifacol.2020.12.1104 Romero-Tapia, G., Fuente, M., & Puig, V. (2018). Leak Localization in Water Distribution Networks using Fisher Discriminant Analysis. IFAC PapersOnLine, 51(24), 929-934. doi:10.1016/j.ifacol.2018.09.686 Sabu, S., Mahinthakumar, G., Ranjithan, R., Levis, J., & Brill, D. (2021). Water Leakage Detection Using Neural Networks. World Environmental and Water Resources Congress 2021. doi:10.1061/9780784483466.096 Santos-Ruiz, I., Blesa, J., Puig, V., & López-Estrada, F. (2020). Leak localization in water distribution networks using classifiers with cosenoidal features. IFAC Papers OnLine, 53(2), 16697-16702. doi:10.1016/j.ifacol.2020.12.1113 Shirzad, A., & Sadegh Safari, M. J. (2019). Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques. Urban Water Journal, 16(9), 653-661. doi:10.1080/1573062X.2020.1713384 Shukla, H., & Piratla, K. (2020). Unsupervised Classification of Flow-Induced Vibration Signals to Detect Leakages in Water Distribution Pipelines. Pipelines 2020, 437-444. doi:10.1061/9780784483190.048 Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. doi:https://doi.org/10.1016/j.jbusres.2019.07.039. Sophocleous, S., Savic, D., & Kapelan, Z. (2019). Leak Localization in a Real Water Distribution Network Based on Search-Space Reduction. Journal of Water Resources Planning and Management, 7(145). doi:10.1061/(ASCE)WR.1943-5452.0001079 Sun, C., Parellada, B., Puig, V., & Cembrano, G. (2019). Leak Localization in Water Distribution Networks Using Pressure and Data-Driven Classifier Approach. Water, 12(54). doi:10.3390/w12010054 Tariq, S., Bakhtawar, B., & Zayed, T. (2022). Data-driven application of MEMS-based accelerometers for leak detection in water distribution networks. Science of the Total Environment, 809. doi:10.1016/j.scitotenv.2021.151110 Tavakoli, R., Sharifara, A., & Najafi, M. (2020). Prediction of Pipe Failures in Wastewater Networks Using Random Forest Classificatio. Pipelines 2020. doi:10.1061/9780784483206.011 United Nations. (2022). Sustainable Development Goals. Obtenido de Goal 6: Ensure access to water and sanitation for all: https://www.un.org/sustainabledevelopment/water-and-sanitation/ van der Walt, J., Heyns, P., & Wilke, D. (2018). Pipe network leak detection: comparison between statistical and machine learning techniques. Urban Water Journal, 15(10), 953-960. doi:10.1080/1573062X.2019.1597375 van der Walt, J., Heyns, P., & Wilke, D. (2021). Model calibration to find leaks in water networks by desensitizing measurements to the model parameters using Artificial Neural Networks. Urban Water Journal, 18(5), 352-363. doi:10.1080/1573062X.2021.1893357 Vanijjirattikhan, R., Khomsay, S., Kitbutrawat, N., Khomsay, K., Supakchukul, U., Udomsuk, S., . . . Anusart, K. (2022). AI-based acoustic leak detection in water distribution systems. Results in Engineering, 15. doi:https://doi.org/10.1016/j.rineng.2022.100557 Wang, X., Guo, G., Liu, S., Wu, Y., Xu, X., & Smith, K. (2020). Burst Detection in District Metering Areas Using Deep Learnin Method. Journal of Water Resources Planning and Management, 146(6). doi:10.1061/(ASCE)WR.1943-5452.0001223. Wu, J., Ma, D., & Wang, W. (2022). Leakage Identification in Water Distribution Networks Based on XGBoost Algorithm. Journal of Water Resources Planning and Management, 148(3). doi:10.1061/(ASCE)WR.1943-5452.0001523 XGBoost Developers. (2022). Introduction to Boosted Trees. Recuperado el 10 de 11 de 2022, de https://xgboost.readthedocs.io/en/latest/tutorials/model.html Zhang, C., Stephens, M., Lambert, M., Alexander, B., & Gong, J. (2022). Acoustic Signal Classification by Support Vector Machine for Pipe Crack Early Warning in Smart Water Networks. Journal of Water Resources Planning and Management, 148(7). doi:10.1061/(ASCE)WR.1943-5452.0001570 Zhou, B., Lau, V., & Wang, X. (2020). Machine-Learning-Based Leakage-Event Identification for Smart Water Supply Systems. IEEE Internet Of Things Journal, 7(3). doi:10.1109/JIOT.2019.2958920 |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Saldarriaga Valderrama, Juan Guillermovirtual::17251-1Acevedo Pérez, Ana Sofíaf5526640-28b4-42ae-a05d-2e612aedc8356002023-03-27T20:39:01Z2023-03-27T20:39:01Z2023-03-27http://hdl.handle.net/1992/65925instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/La revisión bibliográfica se realiza teniendo en cuenta articulos científicos publicados en los últimos 5 años que utilicen machine learning para detectar fugas en sistemas de distribución de agua potable.This paper presents a semi systematic literature review of recent research trends in the detection and prediction of leaks and bursts in water dystribution systems using machine learning, identifying which evaluation metrics and algorithms are most popular, as well as practical applications that have been proposed, and mentioning possible areas for future research.Ingeniero CivilPregrado17 páginasapplication/pdfengUniversidad de los AndesIngeniería CivilFacultad de IngenieríaDepartamento de Ingeniería Civil y AmbientalA review of leak detection and prediction methods in water distribution systems using machine learningTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPLeak detectionLeak predictionMachine learningWater distribution systemsIngenieríaAdanza Dopazo, D., Everson, R., Rogerson, S., Farmani, R., & Dragan, S. (2021). A Leakage Detection System with an Efficient Prioritization at a District Meter Area Level. World Environmental and Water Resources Congress 2021. doi:10.1061/9780784483466.095Alizadeh, Z., Yazdi, S., Mohammadiun, K., Hewage, K., & Sadiq, R. (2019). Evaluation of data driven models for pipe burst prediction in urban water distribution systems. Urban Water Journal, 16(2), 136-145. doi:10.1080/1573062X.2019.1637004American Society of Civil Engineers ASCE. (2022). Special Collection on Battle of the Leakage Detection and Isolation Methods (BattLeDIM). Water Resources Planning and Management. Obtenido de https://ascelibrary.org/jwrmd5/leakage_detection_isolation_methodsAyadi, A., Ghorbel, O., BenSalah, M., & Abid, M. (2019). Kernelized technique for outliers detection to monitoring water pipeline based on WSNs. Computer Networks, 150, 179¿189. doi:10.1016/j.comnet.2019.01.004Ayati, A. H., Haghighi, A., & Ghafouri, H. R. (2022). Machine Learning¿Assisted Model for Leak Detection in Water Distribution Networks Using Hydraulic Transient Flows. Journal of Water Resources Planning and Management, 148(2). doi:10.1061/(ASCE)WR.1943-5452.0001508Aymon, L., Decaix, J., Carrino, F., Mudry, P.-A., Mugellini, E., Khaled, O., & Baltensperger, R. (2019). Leak detection using Random Forest and pressure simulation. 2019 6th Swiss Conference on Data Science (SDS). doi:10.1109/SDS.2019.00008Bohorquez, J., Alexander, B., Simpson, A. R., & Lambert, M. F. (2020). Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks. Journal of Water Resources Planning and Management, 6(146). doi:10.1061/(ASCE)WR.1943-5452.0001187Bohorquez, J., Lambert, M. F., Alexander, B., Simpson, A. R., & Abbott, D. (2022). Stochastic Resonance Enhancement for Leak Detection in Pipelines Using Fluid Transients and Convolutional Neural Networks. Journal of Water Resources Planning and Management, 148(3). doi:10.1061/(ASCE)WR.1943-5452.0001504Bohorquez, J., Simpson, A. R., Lambert, M. F., & Alexander, B. (2021). Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines. Journal of Water Resources Planning and Management, 147(1). doi:10.1061/(ASCE)WR.1943-5452.0001296.Chen, J., Feng, X., & Xiao, S. (2021). An iterative method for leakage zone identification in water distribution networks based on machine learning. Structural Health Monitoring, 20(4), 1938 ¿1956. doi:10.1177/1475921720950470Fan, X., Wang, X., Zhang, X., & Yu, X. (2022). Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors. Reliability Engineering and System Safety, 219. doi:10.1016/j.ress.2021.108185Fan, X., Zhang, X., & Yu, X. (2021). Machine learning model and strategy for fast and accurate detection of leaks in water supply network. Journal of Infrastructure Preservation and Resilience, 2(10). doi:10.1186/s43065-021-00021-6Garðarsson, G. Ö., Boem, F., & Toni, L. (2022). Graph-Based Learning for Leak Detection and Localisation in Water Distribution Networks. IFAC Papers OnLine, 55(6), 661-666. doi:10.1016/j.ifacol.2022.07.203Gonçalves Nascimento Gouveia, C., & Kepler Soares, A. (2021). Water Connection Bursting and Leaks Prediction Using Machine Learning. World Environmental and Water Resources Congress 2021. doi:10.1061/9780784483466.093Guo, G., Yu, X., Liu, S., Ma, Z., Wu, Y., Xu, X., . . . Wu, X. (2021). Leakage Detection inWater Distribution Systems Based on Time-Frequency Convolutional Neural Network. Journal of Water Resources Planning and Management, 147(2). doi:10.1061/(ASCE)WR.1943-5452.0001317Hu, X., Han, Y., Yu, B., Geng, Z., & Fan, J. (2021). Novel leakage detection and water loss management of urban water supply network using multiscale neural networks. 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