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Exploiting capacity of sewer system using decentralized control is a costeffective mean of minimizing the overflow. Given the size of the real sewersystem, exploiting all the installed control structures in the sewer pipes canbe challenging. This paper presents a divide and conquer solution to implementdecentralized control measures based on unsupervised learning algorithms. Asewer system is first divided into a number of subcatchments. A series ofnatural and built factors that have the impact on sewer system performance isthen collected. Clustering algorithms are then applied to groupingsubcatchments with similar hydraulic hydrologic characteristics. Followingwhich, principal component analysis is performed to interpret the main featuresof sub-catchment groups and identify priority control locations. Overflowsunder different control scenarios are compared based on the hydraulic model.Simulation results indicate that priority control applied to the most suitablecluster could bring the most profitable result.

Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction