Sensor Placement in Water Networks: A Stochastic Programming Approach

Abstract

Placement of sensors in water distribution networks helps timely detection of contamination and reduces risk to the population. Identifying the optimal locations of these sensors is important from an economic perspective and has been previously attempted using the theory of optimization. This work extends that formulation by considering uncertainty in the network and describes a stochastic programming method that is capable of determining the optimal sensor location while accounting for demand uncertainties. The problem is formulated as a two stage stochastic programming problem with recourse. The solution to the problem is achieved by using a newly proposed algorithm aimed at efficiently solving stochastic nonlinear programming problems. This makes the problem solution computationally tractable as compared to the traditional stochastic programming methods. The proposed formulation and solution methodology are tested on an example network to perform a comparative study with other formulations. The results show the importance of uncertainty consideration in decision making and highlight the advantages of the proposed stochastic programming approach.

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