Optimal sensor locations in water distribution networks /

Kızıleniş, Güler (2006) Optimal sensor locations in water distribution networks /. [Thesis]

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Official URL: http://risc01.sabanciuniv.edu/record=b1158355 (Table of Contents)


Following the attacks of September 11.2001 in the United States, concerns over terrorist attacks have sharpened The attacks highlighted that public water distribution systems are inherently vulnerable to accidental or intentional water contamination because of their geographical distribution, relative isolation and possible impact to public health. There is an ongoing and fertile research environment aiming to prevent such threats in water distribution systems. One of the key problems arising in The field is To identify sensor locations That minimize the impacts (e.g. detection time, population exposure, contaminated volume consumed) of a terrorist attack aiming the water distribution systems. In this thesis, we present a model of the sensor placement problem in municipal water networks. In our formulation, we focus To The sensor configuration that minimizes the expected time To detection and the expected population exposed. The first objective ensures that all such attacks should be detected in a very short time. We formulate these problems as binary nonlinear objectives. and propose a solution methodology framework based on meta-heuristics. namely, simulated annealing and tabu search. Two different neighborhoods generation mechanisms are utilized in the heuristics. We simulated the contaminant transport in EPANET in order to derive flow and velocity information. A novel approach is introduced in order to handle different flow patterns occurring during the day. In order To ensure the full detection probability, a new concept of restricted sensors that is located at the isolated node in the network is commenced. In the proposed framework, the close relation between clustering and location problems is exploited in The sense that The initial solutions of the meta-heuristics are generated with a k-medoids clustering algorithm.

Item Type:Thesis
Subjects:T Technology > T Technology (General)
ID Code:8301
Deposited By:IC-Cataloging
Deposited On:15 Apr 2008 15:28
Last Modified:25 Mar 2019 16:53

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