Robust blue-green urban flood risk management optimised with a genetic algorithm for multiple rainstorm return periods

TL;DR

This paper develops a multi-objective genetic algorithm framework incorporating multiple rainstorm return periods to optimize Blue-Green Infrastructure (BGI) designs, improving flood risk resilience across various storm magnitudes.

Contribution

It introduces a novel multi-return period optimization methodology using NSGA-II and hydrodynamic modeling, integrating direct damage costs as risk objectives for the first time.

Findings

Composite return period optimization improves performance across all return periods.

Designs optimized for a single return period perform poorly on others.

Climate stress testing confirms robustness of the multi-period optimized BGI.

Abstract

Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit-cost ratios for single rainstorm return periods. However, these schemes may lack robustness in mitigating flood risks across different storm magnitudes. For example, a BGI scheme optimised for a 100-year return period may differ from one optimised for a 10-year return period. This study introduces a novel methodology incorporating five return periods (T = 10, 20, 30, 50, and 100 years) into a multi-objective BGI optimisation framework. The framework combines a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median Risk Difference (MedRD), Maximum Risk Difference (MaxRD), and Area Under Pareto Front (AUPF) reveal that a 100-year optimised BGI design performs poorly when evaluated for other return periods, particularly shorter ones. In contrast, a BGI design optimised using composite return periods enhances performance metrics across all return periods, with the greatest improvements observed in MedRD (22%) and AUPF (73%) for the 20-year return period, and MaxRD (23%) for the 50-year return period. Furthermore, climate uplift stress testing confirms the robustness of the proposed design to future rainfall extremes. This study advocates a paradigm shift in flood risk management, moving from single maximum to multiple rainstorm return period-based designs to enhance resilience and adaptability to future climate extremes.