Scaled Multiobjective Optimization of an Intensive Early Warning System for Water Distribution System Security
Publication: Journal of Hydraulic Engineering
Volume 143, Issue 9
Abstract
Performance of an early warning system composed of online monitoring sensors for protecting municipal water supply is dependent on the number of sensors deployed. The inherent trade-off of performance versus scale of the system implemented is explored in this paper through multiobjective optimization using an augmented messy genetic algorithm (mGA). The augmented messy GA facilitated the comparison of solutions with variability in the number of sensors deployed. In this paper an early warning system is represented by a system of fixed sensors placed at network junctions, inline mobile sensors deployed from network junctions carried by flow within network pipes, and surface transceivers to communicate wirelessly with mobile sensors for data transmission and analysis. Performance of the implemented early warning system was measured as the time required for contamination detection, the detection likelihood, the population affected prior to event detection, and the total system cost for a small-, medium-, and large-scale municipal network. Results show well-defined Pareto fronts for each objective versus the cost of each solution, providing a tool for designers to optimize budget decisions.
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Acknowledgments
This study was supported by the U.S. Binational Science Foundation (BSF), by the Technion Funds for security research, by the joint Israeli Office of the Chief Scientist (OCS) Ministry of Science, Technology and Space (MOST), and by the German Federal Ministry of Education and Research (BMBF), under Project 02WA1298.
References
Banks, M., Wu, R., Salim, W., Hermann, A. C., Staton, R. W., and Porterfield, D. M. (2012). “Real-time monitoring of total residual chlorine in water distribution system based on micro-electro-chemical-sensor.” Institute of Biological Engineering Annual Conf., Institute of Biological Engineering, Lexington, KY.
Berry, J. W., Hart, W. E., Phillips, C. A., and Watson, J. (2006). “A facility location approach to sensor placement optimization.” Proc., 8th Annual Water Distribution Systems Analysis Symp. (WDSA), ASCE, Reston, VA, 1–4.
Chang, N., Bin, P. N., and Ernest, A. (2012). “Optimal sensor deployment in a large-scale complex drinking water network: Comparisons between a rule-based decision support system and optimization models.” Comput. Chem. Eng., 43, 191–199.
Coello Coello, C. A., and Lamont, G. B. (2004). Applications of multi-objective evolutionary algorithms, World Scientific, Singapore.
Conceição, C. M., and Sousa, J. (1999). “Water distribution network design optimization: Simulated annealing approach.” J. Water Resour. Plann. Manage., 215–221.
Dorini, G., Jonkergouw, P., Kapelan, Z., di Pierro, F., Khu, S. T., and Savic, D. (2006). “An efficient algorithm for sensor placement in water distribution systems.” Proc., 8th Annual Water Distribution Systems Analysis Symp. (WDSA), ASCE, Reston, VA, 1–13.
Farmani, R., Walters, G. A., and Savic, D. A. (2005). “Trade-off between total cost and reliability for Anytown water distribution network.” J. Water Resour. Plann. Manage., 161–171.
Goldberg, D., Korb, B., and Deb, K. (1989). “Messy genetic algorithms: Motivation, analysis, and first results.” Complex Syst., 3(5), 493–530.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Boston.
Hadka, D. M., and Reed, P. M. (2013). “Borg: An auto-adaptive many-objective evolutionary computing framework.” Evol. Comput., 21(2), 231–259.
Halhal, D., Walters, G. A., Ouazar, D., and Savic, D. A. (1997). “Water network rehabilitation with structured messy genetic algorithm.” J. Water Resour. Plann. Manage., 137–146.
Hart, W. E., and Murray, R. (2010). “Review of sensor placement strategies for contamination warning systems in drinking water distribution systems.” J. Water Resour. Plann. Manage., 611–619.
Kollat, J. B., and Reed, P. M. (2006). “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design.” Adv. Water Resour., 29(6), 792–807.
Krause, A., Leskovec, J., Guestrin, C., VanBriesen, J. M., and Faloutsos, C. (2008). “Efficient sensor placement optimization for securing large water distribution networks.” J. Water Resour. Plann. Manage., 516–526.
Lai, T., Chen, Y., Huang, P., and Chu, H. (2010). “PipeProbe: A mobile sensor droplet for mapping hidden pipeline.” SenSys ‘10 Proc., 8th ACM Conf. on Embedded Networked Sensor Systems, Association for Computing Machinery, New York.
Lai, T. T.-T., Chen, W.-J., Li, K.-H., Huang, P., and Chu, H.-H. (2012). “TriopusNet: Automating wireless sensor network deployment and replacement in pipeline monitoring.” Proc., 11th ACM Conf. Embedded Sensor Systems, Association for Computing Machinery, New York.
Lee, B. H., and Deininger, R. A. (1992). “Optimal locations of monitoring stations in water distribution system.” J. Environ. Eng., 4–16.
Liu, S., and Auckenthaler, P. (2014). “Optimal sensor placement for event detection and source identification in water distribution networks.” J. Water Supply Res. Tech.-AQUA, 63(1), 51–57.
Maier, H. R., et al. (2014). “Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions.” Environ. Model. Software, 62, 271–299.
Maslia, M. L., Sautner, J. B., Aral, M. M., Reyes, J. J., Abraham, J. E., and Williams, R. C. (2000). “Using water distribution system modeling to assist epidemiologic investigations.” Water Resour. Plann. Manage., 180–198.
Murray, R., Haxton, T., Janke, R., Hart, W., Berry, J., and Phillips, C. (2010). “Sensor network for drinking water contamination warning systems.” National Homeland Security Research Center Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati.
Nicklow, J., et al. (2009). “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage., 412–432.
Oliker, N., and Ostfeld, A. (2015). “Inclusion of mobile sensors in water distribution system monitoring operations.” J. Water Resour. Plann. Manage., 4015044.
Ostfeld, A., et al. (2008). “The Battle of the Water Sensor Networks (BWSN): A design challenge for engineers and algorithms.” J. Water Resour. Plann. Manage., 556–568.
Ostfeld, A., and Salomons, E. (2004). “Optimal layout of early warning detection stations for water distribution systems security.” J. Water Resour. Plann. Manage., 377–385.
Perelman, L., and Ostfeld, A., (2007). “An adaptive heuristic cross-entropy algorithm for optimal design of water distribution systems.” Eng. Optim., 39(4), 413–428.
Perelman, L., and Ostfeld, A. (2013a). “Application of graph theory to sensor placement in water distribution systems.” World Environmental and Water Resources Congress, ASCE, Reston, VA, 617–625.
Perelman, L., and Ostfeld, A., (2013b). “Operation of remote mobile sensors for security of drinking water distribution systems.” Water Res., 47(13), 4217–4226.
Piller, O., Gilbert, D., Melo, L., Strathmann, M., and Flemming, H. C. (2013). “A new concept for placing coupons and sensors for decontamination verification.” World Environmental and Water Resources Congress, ASCE, Reston, VA, 598–610.
Prasad, T. D., and Park, N.-S. (2004). “Multiobjective genetic algorithms for design of water distribution networks.” J. Water Resour. Plann. Manage., 73–82.
Preis, A., and Ostfeld, A. (2006). “Multiobjective sensor design for water distribution systems security.” Proc., 8th Annual Water Distribution Systems Analysis Symp. (WDSA), ASCE, Reston, VA, 1–17.
Preis, A., and Ostfeld, A. (2008). “Multiobjective contaminant response modeling for water distribution systems security.” J. Hydroinf., 10(4), 267–274.
Rasekh, A., Wu, R., Amani, W. W., Salim, W., and Banks, M. K. (2014). “Operation of mobile sensors for monitoring municipal drinking water distribution systems.” World Environmental and Water Resources Congress, ASCE, Reston, VA, 362–376.
Rathi, S., and Gupta, R. (2014). “Monitoring stations in water distribution systems to detect contamination events.” ISH J. Hydraul. Eng., 20(2), 142–150.
Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., and Kollat, J. B. (2013). “Evolutionary multiobjective optimization in water resources: The past, present, and future.” Adv. Water Resour., 51, 438–456.
Sankary, N., et al. (2015). “Mobile sensors for water quality management in water distribution systems.” World Environmental and Water Resources Congress, ASCE, Reston, VA, 792–801.
Savic, D. A., and Walters, G. A. (1997). “Genetic algorithms for least-cost design of water distribution networks.” J. Water Resour. Plann. Manage., 67–77.
Shen, H., and McBean, E. (2011). “Pareto optimality for sensor placements in a water distribution system.” J. Water Resour. Plann. Manage., 243–248.
Sinai, G., Ostfeld, A., and Kessler, A. (1998). “Detecting accidental contaminations in municipal water networks.” J. Water Resour. Plann. Manage., 192–198.
SmartBall Pure Technologies. (2016). “SmartBall leak detection.” ⟨https://www.puretechltd.com/technologies-brands/smartball/smartball-leak-detection⟩ (Dec. 20, 2016).
Suresh, M. A., Stoleru, R., Zechman, E. M., and Shihada, B. (2013). “On event detection and localization in acyclic flow networks.” IEEE Trans. Syst., Man, Cybern.: Syst., 43(3), 708–723.
Suresh, M. A., Zhang, W., Gong, W., Rasekh, A., Stoleru, R., and Banks, M. K. (2015). “Towards optimal monitoring of flow-based systems using mobile wireless sensor networks.” ACM Trans. Sensor Networks, 11(3), 48.
Van Veldhuizen, D. A., and Lamont, G. B. (2000). “Multiobjective evolutionary algorithms: Analyzing the state-of-the-art.” Evol. Comput., 8(2), 125–147.
Weickgenannt, M., Kapelan, Z., Blokker, M., and Savic, D. (2008). “Optimal sensor placement for the efficient contaminant detection in water distribution systems.” Proc., Water Distribution Systems Analysis, ASCE, Reston, VA, 1107–1116.
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Published In
Journal of Hydraulic Engineering
Volume 143 • Issue 9 • September 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 23, 2016
Accepted: Jan 6, 2017
Published online: May 10, 2017
Published in print: Sep 1, 2017
Discussion open until: Oct 10, 2017
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