Optimal Sensor Placement in Smart Sewer Systems Using Network Topology and Elevation

Smart sewer systems consisting of water level sensors can effectively detect overflow events that may present a threat to water quality, human health, and the environment. Designing the optimal layout of the sensor with the minimum number of sensors is essential to minimize the cost and maximize the coverage. This paper proposes a novel methodology to achieve the aim by combining genetic algorithms (GAs) with a blockage detection condition for sensor placements. The blockage detection condition that relies on network topology and elevation information is used to identify the pipe coverage capacity of possible sensor locations. This information serves as input data for the optimization process, which utilizes a GA to identify the optimal sensor placement. The effectiveness of the methodology is verified through its applications to two real gravity sanitary sewer systems with distinct network topologies and elevation information. The results demonstrate that the proposed method can effectively determine the optimal number and placement of sensors required to cover the entire sewer systems throughout multiple investment phases. This contributes to enhancing the effectiveness of investment strategies for smart sewer systems.