A hybrid model-based method for leak detection in large scale water distribution networks

Fereidooni, Zahra, Tahayori, Hooman and Bahadori-Jahromi, Ali ORCID: https://orcid.org/0000-0003-0405-7146 (2020) A hybrid model-based method for leak detection in large scale water distribution networks. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

Preview

PDF
Bahadori-Jahromi_Fereidooni_and_Tahayori_JAIHC_2020_A_hybrid_model-based_method_for_leak_detection_in_large_scale_water_distribution_networks.pdf - Accepted Version
Download (1MB) | Preview

Official URL: https://doi.org/10.1007/s12652-020-02233-2

Abstract

During the past decades, the problem of finding leaks in Water Distribution Networks (WDN) has been controversy. The quicker detection of leaks prevents water loss and helps avoiding their economic and environmental consequences. On the other hand, increasing the speed of leak detection increases the false leak detection that imposes high costs. In this paper, we propose a real-time hybrid method using AI algorithms and hydraulic relations for detecting and locating leaks and identifying the volume of losses material. The proposed method relies on simple and cost-effective flow sensors installed on each junction in the pipeline network. We demonstrate how influential features for leak detection would be generated by using hydraulic equations like Hazen-Williams, Darcy-Weisbach and pressure drop. Through exploiting Decision Tree, KNN, random forest, and Bayesian network we build predictive models and based on the pipeline topology, we locate leaks and their pressure. Comparing the results of applying the proposed method on various leak scenarios shows that the proposed method in this paper, outperforms other existing methods.

Item Type:	Article
Identifier:	10.1007/s12652-020-02233-2
Additional Information:	This is a post-peer-review, pre-copyedit version of an article published in the Journal of Ambient Intelligence and Humanized Computing. The final authenticated version is available online at: https://doi.org/10.1007/s12652-020-02233-2
Keywords:	WDN, Leak, Flow, Pressure, Machine Learning
Subjects:	Computing > Intelligent systems Computing Construction and engineering
Related URLs:	Publisher Publisher
Depositing User:	Ali Bahadori-Jahromi
Date Deposited:	12 Jun 2020 15:28
Last Modified:	28 Aug 2021 07:13
URI:	https://repository.uwl.ac.uk/id/eprint/7038

Downloads

Downloads per month over past year

Actions (login required)

View Item

References

[1] K. B. Adedeji, Y. Hamam, B. T. Abe, and A. M. Abu-Mahfouz, “Towards Achieving a Reliable Leakage Detection and Localization Algorithm for Application in Water Piping Networks: An Overview,” IEEE Access, vol. 5, pp. 20272–20285, 2017, doi: 10.1109/ACCESS.2017.2752802.
[2] H. Ali and J. H. Choi, “A review of underground pipeline leakage and sinkhole monitoring methods based on wireless sensor networking,” Sustain., vol. 11, no. 15, 2019, doi: 10.3390/su11154007.
[3] H. Ali and J. Choi, “Risk Prediction of Sinkhole Occurrence for Different Subsurface Soil Profiles due to Leakage from Underground Sewer and Water Pipelines,” Sustainability, vol. 12, no. 1, p. 310, 2019, doi: 10.3390/su12010310.
[4] S. K. Sinha and M. A. Knight, “Intelligent system for condition monitoring of underground pipelines,” Comput. Civ. Infrastruct. Eng., vol. 19, no. 1, pp. 42–53, 2004, doi: 10.1111/j.1467-8667.2004.00336.x.
[5] A. Sadeghioon, N. Metje, D. Chapman, and C. Anthony, “SmartPipes: Smart Wireless Sensor Networks for Leak Detection in Water Pipelines,” J. Sens. Actuator Networks, vol. 3, no. 1, pp. 64–78, 2014, doi: 10.3390/jsan3010064.
[6] B. Van Hieu, S. Choi, Y. U. Kim, Y. Park, and T. Jeong, “Wireless transmission of acoustic emission signals for real-time monitoring of leakage in underground pipes,” KSCE J. Civ. Eng., vol. 15, no. 5, pp. 805–812, 2011, doi: 10.1007/s12205-011-0899-0.
[7] Y. A. Khulief, A. Khalifa, R. Ben Mansour, and M. A. Habib, “Acoustic Detection of Leaks in Water Pipelines Using Measurements inside Pipe,” J. Pipeline Syst. Eng. Pract., vol. 3, no. 2, pp. 47–54, 2012, doi: 10.1061/(ASCE)PS.1949-1204.0000089.
[8] S. C. Huang, W. W. Lin, M. T. Tsai, and M. H. Chen, “Fiber optic in-line distributed sensor for detection and localization of the pipeline leaks,” Sensors Actuators, A Phys., vol. 135, no. 2, pp. 570–579, 2007, doi: 10.1016/j.sna.2006.10.010.
[9] O. Hunaidi and P. Giamou, “Ground-Penetrating Radar For Detection Of Leaks In Buried Plastic Water Distribution Pipes,” no. May, pp. 27–30, 1998.
[10] R. A. Cody, B. A. Tolson, and J. Orchard, “Detecting Leaks in Water Distribution Pipes Using a Deep Autoencoder and Hydroacoustic Spectrograms,” J. Comput. Civ. Eng., vol. 34, no. 2, pp. 1–8, 2020, doi: 10.1061/(ASCE)CP.1943-5487.0000881.
[11] R. J. Cintra, T. de Oliveira, and M. P. Mintchev, “Leakage Prevention and Real-Time Internal Detection in Pipelines Using a Built-In Wireless Information and Communication Network,” SPE J., no. March 2019, pp. 1–12, 2020, doi: 10.2118/201096-pa.
[12] Y. Liu, X. Ma, Y. Li, Y. Tie, Y. Zhang, and J. Gao, “Water pipeline leakage detection based on machine learning and wireless sensor networks,” Sensors (Switzerland), vol. 19, no. 23, pp. 1–21, 2019, doi: 10.3390/s19235086.
[13] A. Al-Khomairi, “Leak detection in long pipelines using the least squares method Leak detection in long pipelines using the least squares method Détection de fuite dans de longues canalisations en utilisant la méthode des moindres carrés,” J. Hydraul. Res., vol. 463, no. 3, pp. 392–401, 2008, doi: 10.3826/jhr.2008.3191.
[14] P. Taylor, W. Mpesha, M. H. Chaudhry, and S. L. Gassman, “Leak detection in pipes by frequency response method using a step excitation Leak detection in pipes by frequency response method using a step excitation Detection des fuites de tuyauteries par une methode de réponse en frequence utilisant un échelon d ’,” J. Hydraul. Res., no. April 2013, pp. 37–41, 2010.
[15] Y. Asada, M. Kimura, I. Azechi, T. Iida, and N. Kubo, “Leak detection by monitoring pressure to preserve integrity of agricultural pipe,” Paddy Water Environ., vol. 17, no. 3, pp. 351–358, 2019, doi: 10.1007/s10333-019-00730-5.
[16] P. Cuguero-Escofet, J. Blesa, R. Perez, M. Cuguer??-Escofet, and G. Sanz, “Assessment of a leak localization algorithm in water networks under demand uncertainty,” IFAC-PapersOnLine, vol. 28, no. 21, pp. 226–231, 2015, doi: 10.1016/j.ifacol.2015.09.532.
[17] A. Soldevila, R. M. Fernandez-Canti, J. Blesa, S. Tornil-Sin, and V. Puig, “Leak localization in water distribution networks using Bayesian classifiers,” J. Process Control, vol. 55, pp. 1–9, 2017, doi: 10.1016/j.jprocont.2017.03.015.
[18] A. Soldevila, S. Tornil-sin, J. Blesa, M. Rosa, and V. Puig, “Modeling and Monitoring of Pipelines and Networks,” Springer Int. Publ. AG 2017 C. Verde L. Torres (eds.), Model. Monit. Pipelines Networks, Appl. Cond. Monit., vol. 7, 2017, doi: 10.1007/978-3-319-55944-5.
[19] J. Mashford, D. De Silva, D. Marney, and S. Burn, “An approach to leak detection in pipe networks using analysis of monitored pressure by support vector machine,” Third Int. Conf. Netw. Syst. Secur., no. Figure 1, pp. 534–539, 2009, doi: 10.1109/NSS.2009.38.
[20] S. G. Buchberger and G. Nadimpalli, “Leak Estimation in Water Distribution Systems by Statistical Analysis of Flow Readings,” J. Water Resour. Plan. Manag., vol. 130, no. 4, pp. 321–329, 2004, doi: 10.1061/(ASCE)0733-9496(2004)130:4(321).
[21] G. Mazzolani, L. Berardi, D. Laucelli, A. Simone, R. Martino, and O. Giustolisi, “Estimating Leakages in Water Distribution Networks Based Only on Inlet Flow Data,” J. Water Resour. Plan. Manag., pp. 1–11, 2017, doi: 10.1061/(ASCE)WR.1943-5452.0000758.
[22] T. R. Sheltami, A. Bala, and E. M. Shakshuki, “Wireless sensor networks for leak detection in pipelines: a survey,” J. Ambient Intell. Humaniz. Comput., vol. 7, no. 3, pp. 347–356, 2016, doi: 10.1007/s12652-016-0362-7.
[23] M. A. Adegboye, W. K. Fung, and A. Karnik, “Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches,” Sensors (Switzerland), vol. 19, no. 11, 2019, doi: 10.3390/s19112548.
[24] “Hazen Williams formula for use in fire sprinkler systems.” https://www.canutesoft.com/Hydraulic-calculation-for-fire-protection-engineers/the-hazen-williams-formula-for-use-in-fire-sprinkler-systems.html (accessed Sep. 26, 2017).
[25] B. E. Larock, R. W. Jeppson, and G. Z. Watters, Hydraulics of Pipeline Systems. 1999.
[26] A. Jain and D. Zongker, “Feature Selection: Evaluation, Application, and Small Sample Performance,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 2, pp. 153–158, 1997, doi: 10.1109/34.574797.
[27] L. Yu and H. Liu, “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution,” Int. Conf. Mach. Learn., pp. 1–8, 2003, doi: citeulike-article-id:3398512.
[28] ScikitLearn, “1.13. Feature selection — scikit-learn 0.21.3 documentation.” https://scikit-learn.org/stable/modules/feature_selection.html#variance-threshold (accessed Aug. 25, 2019).
[29] H. Jiawei, M. Kamber, and J. Pei, Data Mining Concepts and Techniques. 2012.
[30] N. Suguna and K. Thanushkodi, “An Improved k-Nearest Neighbor Classification Using Genetic Algorithm,” Int. J. Comput. Sci. Issues, vol. 7, no. 4, pp. 18–21, 2010.
[31] F. Amalina, N. Ali, N. Badrul, and A. Abdullah, “Evaluation of machine learning classifiers for mobile malware detection,” Soft Comput., vol. 20, no. 1, pp. 343–357, 2014, doi: 10.1007/s00500-014-1511-6.
[32] D. Tomar and S. Agarwal, “A survey on Data Mining approaches for Healthcare,” Bio-Science and Bio-Technology, vol. 5, no. 5, pp. 241–266, 2013.
[33] “Big data helpen slim waterleidingnetwerk | Waterbedrijf Vitens.” https://www.vitens.com/pers-en-nieuws/nieuwsoverzicht/persberichten/big-data-helpen-slim-waterleidingnetwerk (accessed Mar. 28, 2020).
[34] S. R.P., N. V.E, and J. Amaranath, “Feasibility Analysis And Design Of Water Distribution System For Tiurnelveli Corporation Using Loop and WATER GEMS Software,” Int. J. Appl. Bioeng., vol. 7, no. 1, pp. 61–70, 2013, [Online]. Available: http://www.academia.edu/5905702/Feasibility_Analysis_And_Design_Of_Water_Distribution_System_For_Tiurnelveli_Corporation_Using_Loop_and_WATER_GEMS_Software_..

Tools

CORE (COnnecting REpositories)

The University of West London

A hybrid model-based method for leak detection in large scale water distribution networks

Abstract

Downloads

Actions (login required)

Menu