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Combined Sewer Overflow (CSO) is a major problem to be addressed by manycities. Understanding the behavior of sewer system through proper urbanhydrological models is an effective method of enhancing sewer systemmanagement. Conventional deterministic methods, which heavily rely on physicalprinciples, is inappropriate for real-time purpose due to their expensivecomputation. On the other hand, data-driven methods have gained huge interests,but most studies only focus on modeling a single component of the sewer systemand supply information at a very abstract level. In this paper, we proposed theDeepCSO model, which aims at forecasting CSO events from multiple CSOstructures simultaneously in near real time at a citywide level. The proposedmodel provided an intermediate methodology that combines the flexibility ofdata-driven methods and the rich information contained in deterministic methodswhile avoiding the drawbacks of these two methods. A comparison of the resultsdemonstrated that the deep learning based multi-task model is superior to thetraditional methods.

DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning