2023/05/29 by Abhiram Mullapudi, Mullapudi, Abhiram, Branko Kerkez +1
Engineering · Environmental Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Flood Risk Assessment and Management #Machine Learning (cs.LG) #Systems and Control (eess.SY) #Urban Stormwater Management Solutions #Water Systems and Optimization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.18630
openalex publication_date 2023/05/29 · openalex created_date 2023/06/01 · openalex updated_date 2026/07/28
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.