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Lookup NU author(s): Asid Ur Rehman, Dr Vassilis GlenisORCiD, Professor Chris Kilsby, Professor Claire WalshORCiD
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© 2025 The Authors.Semi-distributed hydrodynamic models combined with optimisation methods are often proposed for cost-effective pond designs, but typically oversimplify pond representation, neglecting complex urban catchment dynamics. This study addresses these limitations by introducing an integrated optimisation framework that couples a fully distributed hydrodynamic flood model with a Non-dominated Sorting Genetic Algorithm (NSGA-II). The fully distributed approach explicitly captures spatio-temporal surface flow dynamics across a high-resolution computational grid for the catchment, enhancing pond location screening and its subsequent representation in hydrodynamic simulations for flood risk reduction. The optimisation process adjusts locations and sizes to minimise direct damage cost (DDC) and expected annual damage (EAD) relative to pond life cycle cost (LCC), evaluated across 100-year and composite storm scenarios. An enhanced use of standard and marginal benefit-cost ratio (BCR) metrics identifies a set of high-performing solutions from the composite Pareto front. Results indicate ponds near flood receptors are generally more effective, while the optimisation for a 60-min storm achieves significantly higher damage reduction than a 30-min event when evaluated for a 100-year storm. In the 60-min composite storm optimisation, the high-performing solution that achieves the greatest damage reduction requires only ∼37 % of the designed storage capacity for the current study, highlighting efficient resource allocation by the optimisation and solution selection framework. This study demonstrates the critical importance of fully distributed modelling integrated with evolutionary optimisation methods in accurately assessing the location and size of detention ponds, offering urban planners and decision-makers a robust, practical tool for designing cost-effective and resilient flood risk management strategies.
Author(s): Ur Rehman A, Glenis V, Lewis E, Kilsby C, Walsh C
Publication type: Article
Publication status: Published
Journal: International Journal of Disaster Risk Reduction
Year: 2025
Volume: 131
Print publication date: 01/12/2025
Online publication date: 10/11/2025
Acceptance date: 05/11/2025
ISSN (electronic): 2212-4209
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.ijdrr.2025.105901
DOI: 10.1016/j.ijdrr.2025.105901
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