  Combined Sewer Overflow (CSO) is a major problem to be addressed by many
cities. Understanding the behavior of sewer system through proper urban
hydrological models is an effective method of enhancing sewer system
management. Conventional deterministic methods, which heavily rely on physical
principles, is inappropriate for real-time purpose due to their expensive
computation. On the other hand, data-driven methods have gained huge interests,
but most studies only focus on modeling a single component of the sewer system
and supply information at a very abstract level. In this paper, we proposed the
DeepCSO model, which aims at forecasting CSO events from multiple CSO
structures simultaneously in near real time at a citywide level. The proposed
model provided an intermediate methodology that combines the flexibility of
data-driven methods and the rich information contained in deterministic methods
while avoiding the drawbacks of these two methods. A comparison of the results
demonstrated that the deep learning based multi-task model is superior to the
traditional methods.
