Abstract
Urban water supply network is easily affected by intentional or occasional chemical and biological pollution, which threatens the health of consumers. In recent years, drinking water contamination happens occasionally, which seriously harms social stabilization and safety. Placing sensors in water supply pipes can monitor water quality in real time, which may prevent contamination accidents. However, how to reversely locate pollution sources through the detecting information from water quality sensors is a challengeable issue. Its difficulties lie in that limited sensors, massive pipe network nodes and dynamic water demand of users lead to the uncertainty, large-scale and dynamism of this optimization problem. This paper mainly studies the uncertainty problem in contaminant sources identification (CSI). The previous study of CSI supposes that hydraulic output (e.g., water demand) is known. Whereas, the inherent variability of urban water consumption brings an uncertain problem that water demand presents volatility. In this paper, the water demand of water supply network nodes simulated by Gaussian model is stochastic, and then being used to solve the problem of CSI, simulation–optimization method finds the minimum target of CSI and concentration which meet the simulation value and detected value of sensors. This paper proposes an improved genetic algorithm to solve the CSI problem under uncertainty water demand and comparative experiments are placed on two water distribution networks of different sizes.
Published Version
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