Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction
This work addresses overflow management in sewer systems for urban infrastructure, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of minimizing sewer overflow by exploiting system capacity through decentralized control, using unsupervised learning and dimensionality reduction to identify priority control locations, which simulation results showed could yield the most profitable outcomes.
Exploiting capacity of sewer system using decentralized control is a cost effective mean of minimizing the overflow. Given the size of the real sewer system, exploiting all the installed control structures in the sewer pipes can be challenging. This paper presents a divide and conquer solution to implement decentralized control measures based on unsupervised learning algorithms. A sewer system is first divided into a number of subcatchments. A series of natural and built factors that have the impact on sewer system performance is then collected. Clustering algorithms are then applied to grouping subcatchments with similar hydraulic hydrologic characteristics. Following which, principal component analysis is performed to interpret the main features of sub-catchment groups and identify priority control locations. Overflows under different control scenarios are compared based on the hydraulic model. Simulation results indicate that priority control applied to the most suitable cluster could bring the most profitable result.