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- W4285244673 abstract "Bioretention cells (BRCs) are an emerging technology used for urban stormwater management. Existing design guidelines for BRCs offer a range of design parameters values, varying by region and climate conditions, meaning manual BRC design can be iterative and time consuming, with an optimal solution being difficult to arrive at. Modelling BRC designs is typically performed using the Stormwater Management Model (SWMM), which can be time consuming given the iterative design process and nature of physical-based models. In this research, a Surrogate Model (SM) is proposed as an alternative tool for designing and modelling BRCs. SMs are approximate representations of more complex models. The SM is constructed by generating outputs from the original model (SWMM) on a set of known values and subsequently training the SM. Therefore, SM predictive performance is highly dependent on the training dataset, yet questions remain on how to select the best training set for optimal performance. Following a case study for BRC design in the City of Toronto, an Artificial Neural Network (ANN) was used as an SM to SWMM. Latin Hypercube Sampling (LHS) was used to generate training samples ranging from 50 to 200 samples for 18 rainfall events. Results indicate that the SM can very accurately replicate SWMM results with Nash–Sutcliffe Efficiency ranging from 0.97 to 0.99. Additionally, a comparison of performance for SMs revealed that using 50 samples yields superior performance compared to larger samples. Thus, the developed SM can be reliably integrated into a simulation-optimisation framework for BRC optimal design." @default.
- W4285244673 created "2022-07-14" @default.
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- W4285244673 date "2022-01-01" @default.
- W4285244673 modified "2023-09-27" @default.
- W4285244673 title "Surrogate Model Development for Bioretention Cell Simulation-Optimisation Applications" @default.
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- W4285244673 doi "https://doi.org/10.1007/978-981-19-1065-4_17" @default.
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