Evolutionary Computation Techniques To Assess Losses In Water Supply Networks

In this work, a methodology that combines both simulation and measurements of pressures and discharges on the water supply network is applied to a case study. The demands of the model nodes are systematically changed by means of two evolutionary algorithms and the network is simulated in order to match the readings of the instruments. By comparing the measurements with the simulated values it is possible to assess the losses and estimate their locations. Specifically, two methods have been tested and applied to a case study: the former based on Simulated Annealing (SA) and the latter on Genetic Algorithms (GAs). The simulations show that the methods based on GAs perform much better and are able to detect the different hypothesized scenarios, while the single individual used by SA risks being trapped in an unfeasible zone in its search. Moreover, the solution obtained by GAs can be further improved by means of a simple Hill Climbing procedure (HC), thus achieving satisfactory results. © 2013 WIT Press.171119128WIT Transactions on Ecology and The Environment,International Journal of Sustainable Development and PlanningAlvisi, S., Grata, S., Franchini, M., Leakage detection planning in water distribution systems (2006) Management of Water N- Proceedings of the Conference Efficient Management of Water Networks, , Design and Rehabilitation Techniques, De Angeli Editore, Milano, ItalyHalhal, D., Walters, G.A., Savic, D.A., Ouazar, D., Scheduling of water distribution system rehabilitation using structured messy genetic algorithms (1999) Evolutionary Computation, 7 (3)Giustolisi, O., Laucelli, D., Savic, D., Development of rehabilitation plans for water mains replacement considering risk and cost-benefit assessment (2006) J of Civil Engineering and Environmental Systems, 23 (3), pp. 175-190Engelhardt, M., Skipworth, P., Savic, D., Saul, A., Walters, G., Rehabilitation strategies for water distribution networks: A literature review with a uk perspective (2000) Urban Water Journal, 2, pp. 153-170Male, J.W., Walski, T.M., Slutsky, A.H., Analyzingwater main replacement policies (1990) J Water Resour Plan Manage, 116 (3), pp. 362-374Sundahl, A., Using break data on water pipe systems for renewal planning. COST Action C3 workshop (1996) BrusselsMambretti, S., Orsi, E., Genetic Algorithms For Leak Detection In Water Supply Networks (2012) 1st International Conference On Urban Water, pp. 25-27. , New Forest, UKBack, T., Fogel, D., Michalewicz, Z., (1997) Handbook of Evolutionary Computation, , IOP Publishing Ltd. and Oxford University Press, New York and OxfordHolland, J., Outline for a logical theory of adaptive systems (1962) Journal of the ACM, 9 (3)Holland, J., (1975) Adaptation In Natural and Artificial Systems, , University of Michigan Pressvan Laarhoven, P., Aarts, E., (1987) Simulated Annealing: Theory and Applications, , SpringerZhang, L., Zhou, C., Liu, X.Z.M., Ma, M., Liang, Y., Solving multi objective optimization problems using particle swarm optimization (2003) Proceedings of IEEE Congress On Evolutionary Computation 2003 (CEC 2003), pp. 2400-2405. , Canbella, AustraliaDorigo, M., Sẗutzle, T., (2004) Ant Colony Optimization, p. 319. , The MIT PressGoldberg, D., (1989) Genetic Algorithms In Search, Optimization and Machine Learning, , Massachusetts: Addison-Wesley, Readin