The goal of sensor placement in water networks is to detect contamination of intentional and accidental events, and to ensure safe and quality drinking water for consumers. Solving this problem as a classical optimization problem has been the focus of attention for many years. This paper presents a new modeling approach by taking into account that a water network is a complex network. Thus, the structural and dynamical complexity properties should play a role in our sensor placement problem. We model the sensor placement problem by a novel integrated approach using both the classical optimization and complex network theory. To test our modeling approach, we have considered a well-known water network with 129 nodes and 178 links which have been researched by about 20 other research groups.

A complex network theory approach for optimizing contamination warning sensor location in water distribution networks

Zio, Enrico
2018-01-01

Abstract

The goal of sensor placement in water networks is to detect contamination of intentional and accidental events, and to ensure safe and quality drinking water for consumers. Solving this problem as a classical optimization problem has been the focus of attention for many years. This paper presents a new modeling approach by taking into account that a water network is a complex network. Thus, the structural and dynamical complexity properties should play a role in our sensor placement problem. We model the sensor placement problem by a novel integrated approach using both the classical optimization and complex network theory. To test our modeling approach, we have considered a well-known water network with 129 nodes and 178 links which have been researched by about 20 other research groups.
2018
Complex networks; Contamination warning sensors; Optimal location problem; Water distribution network; Geotechnical Engineering and Engineering Geology; Safety Research; Geology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1077941
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