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Lookup NU author(s): Asid Ur Rehman, Dr Vassilis Glenis, Dr Elizabeth Lewis, Emeritus Professor Chris Kilsby
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s)Designing city-scale Blue-Green Infrastructure (BGI) for flood risk management requires detailed and robust methods. This is due to the complex interaction of flow pathways and the need to assess cost-benefit trade-offs for various BGI options. This study aims to find a cost-effective BGI placement scheme by developing an improved approach called the Cost OptimisatioN Framework for Implementing blue-Green infrastructURE (CONFIGURE). The optimisation framework integrates a detailed hydrodynamic flood simulation model with a multi-objective optimisation algorithm (Non-dominated Sorting Genetic Algorithm II). The use of a high-resolution flood simulation model ensures the explicit representation of BGI and other land use features to simulate flow pathways and surface flood risk accurately, while the optimisation algorithm guarantees achieving the best cost-benefit trade-offs for given BGI options. The current study uses the advanced CityCAT hydrodynamic flood model to evaluate the efficiency of the optimisation framework and the impact of location and size of permeable interventions on the optimisation process and subsequent cost-benefit trade-offs. This is achieved by dividing permeable surface areas into intervention zones of varying size and quantity. Furthermore, rainstorm events with 100-year and 30-year return periods are analysed to identify any common optimal solutions for different rainfall intensities. Depending on the number of intervention locations, the automated framework reliably achieves optimal BGI implementation solutions in a fraction of the time required to find the best solutions by trialling all possible options. Designing and optimising interventions with smaller sizes but many permeable zones save a good fraction of investment. However, such a design scheme requires more computational time to find optimal options. Furthermore, the optimal spatial configuration of BGI varies with different rainstorm severities, suggesting a need for careful selection of the rainstorm return period. Based on the results, CONFIGURE shows promise in devising sustainable urban flood risk management designs.
Author(s): Ur Rehman A, Glenis V, Lewis E, Kilsby C
Publication type: Article
Publication status: Published
Journal: Journal of Hydrology
Year: 2024
Volume: 638
Print publication date: 01/07/2024
Online publication date: 22/06/2024
Acceptance date: 02/04/2024
Date deposited: 08/07/2024
ISSN (print): 0022-1694
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.jhydrol.2024.131571
DOI: 10.1016/j.jhydrol.2024.131571
Data Access Statement: Python code for CONFIGURE is available on GitHub: https://github.com/asidurrehman/configure10
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