  Dynamic control is emerging as an effective methodology for operating
stormwater systems under stress from rapidly evolving weather patterns.
Informed by rainfall predictions and real-time sensor measurements, control
assets in the stormwater network can be dynamically configured to tune the
behavior of the stormwater network to reduce the risk of urban flooding,
equalize flows to the water reclamation facilities, and protect the receiving
water bodies. However, developing such control strategies requires significant
human and computational resources, and a methodology does not yet exist for
quantifying the risks associated with implementing these control strategies. To
address these challenges, in this paper, we introduce a Bayesian
Optimization-based approach for identifying stormwater control strategies and
estimating the associated uncertainties. We evaluate the efficacy of this
approach in identifying viable control strategies in a simulated environment on
real-world inspired combined and separated stormwater networks. We demonstrate
the computational efficiency of the proposed approach by comparing it against a
Genetic algorithm. Furthermore, we extend the Bayesian Optimization-based
approach to quantify the uncertainty associated with the identified control
strategies and evaluate it on a synthetic stormwater network. To our knowledge,
this is the first-ever stormwater control methodology that quantifies
uncertainty associated with the identified control actions. This Bayesian
optimization-based stormwater control methodology is an off-the-shelf control
approach that can be applied to control any stormwater network as long we have
access to the rainfall predictions, and there exists a model for simulating the
behavior of the stormwater network.
