English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Dynamic control is emerging as an effective methodology for operatingstormwater systems under stress from rapidly evolving weather patterns.Informed by rainfall predictions and real-time sensor measurements, controlassets in the stormwater network can be dynamically configured to tune thebehavior of the stormwater network to reduce the risk of urban flooding,equalize flows to the water reclamation facilities, and protect the receivingwater bodies. However, developing such control strategies requires significanthuman and computational resources, and a methodology does not yet exist forquantifying the risks associated with implementing these control strategies. Toaddress these challenges, in this paper, we introduce a BayesianOptimization-based approach for identifying stormwater control strategies andestimating the associated uncertainties. We evaluate the efficacy of thisapproach in identifying viable control strategies in a simulated environment onreal-world inspired combined and separated stormwater networks. We demonstratethe computational efficiency of the proposed approach by comparing it against aGenetic algorithm. Furthermore, we extend the Bayesian Optimization-basedapproach to quantify the uncertainty associated with the identified controlstrategies and evaluate it on a synthetic stormwater network. To our knowledge,this is the first-ever stormwater control methodology that quantifiesuncertainty associated with the identified control actions. This Bayesianoptimization-based stormwater control methodology is an off-the-shelf controlapproach that can be applied to control any stormwater network as long we haveaccess to the rainfall predictions, and there exists a model for simulating thebehavior of the stormwater network.

Identification of stormwater control strategies and their associated uncertainties using Bayesian Optimization