Enhancing Inflow and Infiltration Detection in Urban Sewer Networks with a New Deterministic Sensor Placement Method

Abstract

The inflow and infiltration (I&I) is an issue for many urban sewer networks (USNs), which can significantly affect system functioning. Placing sensors within the USNs is a typical approach to detect large I&I event, but deploying a limited number of sensors while achieving maximum detection reliability is challenging. While some methods are available for sensor placement, they are generally heuristic search-based methods (HSBMs) and hence the resultant sensor placement strategies (SPSs) are variable over different algorithm runs or parameterizations. This paper develops a new deterministic two-stage clustering method for SPS optimization based on information entropy. Within the first stage, the Spectral Clustering method is applied to assign USN nodes to different clusters according to their joint entropy. In the second stage, the topology structure property is considered to enable further clustering for improving detection reliability. Average I&I detection reliability is used to select clusters and the optimal SPS is identified by maximizing joint entropy of all possible solutions where a single sensor is assigned to each selected cluster. The proposed method and two existing HSBMs are applied to a real USN and their performance is compared. The results obtained show that: (i) a strong correlation coefficient R (R>0.95) is observed between joint entropy and SPS's detection reliability, which has not been revealed before, (ii) the proposed method consistently outperforms the other two approaches in efficiently offering SPSs with about 7-15% higher detection reliability, and (iii) the proposed method provides the optimal SPS in a deterministic manner, which makes it attractive for engineering applications.