Optimising Urban Flood Resilience

2026-04-17

AI Summary

Urban flood risk is intensifying, and while blue-green infrastructure (BGI) offers significant mitigation potential, its optimal deployment is hindered by high-dimensional complexity. This study proposes an innovative framework that couples a fully dynamic, high-resolution hydrodynamic model with a tailored multi-objective evolutionary algorithm to enable efficient BGI optimization. The approach achieves automatic layout design in complex urban settings while drastically reducing the number of required simulations. It delivers precise convergence metrics within solvable search spaces and outperforms existing benchmark algorithms in unsolvable ones, substantially enhancing both computational efficiency and solution reliability. By doing so, the framework provides decision-makers with scientifically robust and actionable investment strategies for effective flood risk management.

Technology Category

• Search and Optimization: Evolutionary Computation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Due to the increasing frequency and severity of storm events, driven by the escalation of anthropogenic climate change and urban expansion, there is a requirement for increasingly efficient flood risk management strategies. While Blue-Green Infrastructure (BGI) offers a sustainable solution for managing flood risk, optimal implementation is challenging. To help overcome this challenge, this study presents a novel multi-objective optimisation tool that couples a state-of-the-art hydrodynamic model with a bespoke evolutionary algorithm. The use of a fully dynamic hydrodynamic model enables the tool to accurately evaluate the effectiveness of proposed BGI features with respect to property scale flood vulnerability and hazard analysis. This contrasts with alternative approaches which utilise simplified models, which can only reliably predict inundation extents, thus the proposed optimisation tool provides greater certainty regarding the optimality of the solutions. As a hydrodynamic simulation is required to evaluate each candidate solution, the bespoke evolutionary algorithm is specifically designed to minimise the number of simulations required, ensuring the tool is computationally practical. The effectiveness of the tool in this regard is validated via the derivation of exact convergence measures, for a tractable search space, and via comparisons with benchmark algorithms, for an intractable search space. Compared with traditional design practices, the proposed tool offers an automated approach capable of efficiently exploring a wide range of solutions, providing decision-makers with a set of optimal solutions from which they can make informed investment decisions. The presented methods provide a robust framework for optimising a variety of BGI features in complex urban environments.

Problem

• Urban Flood Resilience
• Blue-Green Infrastructure
• Flood Risk Management
• Climate Change
• Urban Expansion

Innovation

• Blue-Green Infrastructure
• multi-objective optimisation
• hydrodynamic modelling
• evolutionary algorithm
• urban flood resilience