Burst Detection Using an Artificial Immune Network in Water-Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 10
Abstract
A new method using artificial immune network is presented to identify pipe burst in water-distribution systems. Burst detection is considered as the problem of pattern recognition in the proposed method. An artificial database that includes information on burst events (BEs) is first established. Using the clonal selection algorithm, the artificial immune network is constructed based on the principle of immune system. The burst location is finally identified using the nearest neighbor method. Three offline case studies are illustrated in detail to evaluate the current method. A total of five possible burst locations are identified from 34 nodes in Case Study 1, whereas four possible burst locations are identified from 77 nodes in Case Study 2. The results derived from the first two case studies show that the method can identify the possible burst areas, including the true burst location, using model-simulated results. The data derived from real BEs in Case Study 3 are used to evaluate the proposed method, through which performance of the method is further investigated. Based on all three case studies, the proposed method has the potential to be a useful tool for burst detection.
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Acknowledgments
This work was financially supported by the National Major Science and Technology Program for Water Pollution Control and Treatment (Grant No. 2012ZX07408-002).
References
Aksela, K., Aksela, M., and Vahala, R. (2009). “Leakage detection in a real distribution network using a SOM.” Urban Water J., 6(4), 279–289.
Caputo, A. C., and Pelagagge, P. M. (2002). “An inverse approach for piping networks monitoring.” J. Loss Prev. Process Ind., 15(6), 497–505.
Caputo, A. C., and Pelagagge, P. M. (2003). “Using neural networks to monitor piping systems.” Process Saf. Prog., 22(2), 119–127.
Dasgupta, D. (1997). “Artificial neural network and artificial immune systems: Similarities and differences.” IEEE Int. Conf. Computational Cybernetics and Simulation, New York, 873–878.
Dasgupta, D. (1998). “An artificial immune system as a multi-agent decision support system.” IEEE Int. Conf. on Systems, Man, and Cybernetics, New York, 3816–3820.
De Castro, L. N., and Von Zuben, F. J. (2002). “Learning and optimization using the clonal selection principle.” IEEE Trans. Evol. Comput., 6(3), 239–251.
Farley, B., Mounce, S. R., and Boxall, J. R. (2013). “Development and field validation of a burst localisation methodology.” J. Water Resour. Plann. Manage., 604–613.
Forrest, S., Perelson, A., Cherukuri, R. (1994). “Self non-self discrimination in a computer.” Proc., 1994 IEEE Computer Society Symp. on Research in Security and Privacy, IEEE Computer Society, Los Alamitos, CA, 202–212.
Kim, J., and Bentley, P. (2001). “An evaluation of negative selection in an artificial immune system for network intrusion detection.” Proc., Genetic and Evolutionary Computation Conf. (GECCO), San Francisco, 1330–1337.
King, R. L., Russ, S. H., Lambert, A. B., and Reese, D. S. (2001). “An artificial immune system model for intelligent agents.” Future Gener. Comput. Syst., 17(4), 335–343.
Kumar, K. K., and Ndidhoefer, J. (1997). “Immunized adaptive critics for level 2 intelligent control.” 1997 IEEE Int. Conf. on Computer Cybernetics and Simulation, New York, 856–861.
Mashford, J., De Silva, D., Marney, D., and Burn, S. (2009). “An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine.” Proc., 3rd Int. Conf. on Network and System Security, IEEE, Gold Coast, Australia, 534–539.
Meshref, H., and VanLandingham, H. (2000). “Artificial immune systems: Application to autonomous agents.” 2000 IEEE Int. Conf. on Systems, Man and Cybernetics, IEEE, New York, 61–66.
Mounce, S. R., Boxall, J., and Machell, J. (2010). “Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows.” J. Water Resour. Plann. Manage., 309–318.
Mounce, S. R., Day, A. J., Wood, A. S., Khan, A., Widdop, P. D., and Machell, J. (2002). “A neural network approach to burst detection.” Water Sci. Technol., 45(4–5), 237–246.
Pathirana, A. (2010). “Epanet2 desktop application for pressure driven demand modeling.” Proc., WDSA 2010, ASCE, Reston, VA, 65–74.
Romano, M., Kapelan, Z., and Savic, D. A. (2009). “Bayesian-based online burst detection in water distribution systems.” Integrating Water Systems: Proc. Computer and Control in the Water Industry 2009, J. Boxall and C. Maksimović, eds., CRC Press, FL, 331–337.
Romano, M., Kapelan, Z., and Savi, D. A. (2011). “Burst detection and location in water distribution systems.” World Environmental and Water Resources Congress, ASCE, Reston, VA, 1–10.
Rossman, L. A. (2002). EPANET 2.0 users manual, National Risk Management Research Laboratory, U.S. EPA, Cincinnati.
Shinozuka, M., Liang, J., and Feng, M. Q. (2005). “Use of supervisory control and data acquisition for damage location of water delivery systems.” J. Eng. Mech., 225–230.
Wu, Z. Y., et al. (2008). “Leak detection case study by means of optimizing emitter locations and flows.” Proc., 10th Annual Int. Symp. on Water Distribution Systems Analysis, ASCE, Reston, VA, 1–11.
Wu, Z. Y., and Sage, P. (2006). “Water loss detection via genetic algorithm optimization-based model calibration.” Proc., 8th Annual Int. Symp. on Water Distribution Systems Analysis, ASCE, Reston, VA, 1–11.
Wu, Z. Y., and Sage, P. (2007). “Pressure dependent demand optimization for leakage detection in water distribution systems.” Proc., 9th Int. Conf. on Computing and Control in the Water Industry, ASCE, Reston, VA, 353–361.
Wu, Z. Y., Sage, P., and Turtle, D. (2010). “Pressure-dependent leak detection model and its application to a district water system.” J. Water Resour. Plann. Manage., 116–128.
Wu, Z. Y., Walski, T., Mankowski, R., Cook, J., Tryby, M., and Herrin, G. (2002). “Calibrating water distribution model via genetic algorithms.” Proc., AWWA IMTech Conf, AWWA, Denver, CO, 1–10.
Ye, G., and Fenner, R. A. (2011). “Kalman filtering of hydraulic measurements for burst detection in water distribution systems.” J. Pipeline Syst. Eng. Pract., 14–22.
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© 2014 American Society of Civil Engineers.
History
Received: Apr 10, 2012
Accepted: Aug 23, 2013
Published online: Aug 27, 2013
Published in print: Oct 1, 2014
Discussion open until: Oct 15, 2014
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