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Multi-objective optimisation framework for Blue-Green Infrastructure placement using detailed flood model

Lookup NU author(s): Asid Ur Rehman, Dr Vassilis Glenis, Dr Elizabeth Lewis, Emeritus Professor Chris Kilsby

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

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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Funding

Funder referenceFunder name
Natural Environmental Research Council
ONE Planet Doctoral Training Partnership
NE/S007512/1Natural Environment Research Council (NERC)

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