Abstract:
Despite significant advancements in flood risk assessment and damage monetization, research is lacking for simultaneously examining the impacts of the complexity of factors such as rising sea levels, changing rainfall patterns, and urbanization, on flood damage assessment. This study adopts an innovative staging procedure that progressively and strategically optimizes flood damage mitigation measures while addressing the uncertainties associated with the implementation of flood mitigation measures over three different time horizons (2040, 2070, and 2100), with each subsequent stage refined based on the constraints and optimal results of the previous stage. Using the Non-dominated Sorting Genetic Algorithm (NSGA II), the study compares 27 optimized pathways for mitigating flood damages, balancing investment costs and Average Annual Damage (AAD) reduction. The results demonstrate that the proposed approach achieves an AAD reduction of up to 2.89% in 2040, 4.03% in 2070, and 2.12% in 2100 under the most comprehensive mitigation pathways while balancing the costs. The study highlights cost-effective alternatives, such as combining dredging and permeable asphalt, achieving a 1.31% AAD reduction in 2040 with no additional costs. Compared to static single-stage mitigation policies, the proposed staging approach offers greater flexibility and efficiency in addressing dynamic urbanization and climate change scenarios. These results underline the trade-offs between cost and effectiveness, equipping policymakers with a robust decision-making framework to tailor flood mitigation strategies for diverse global contexts. Overall, this study significantly advances the strategic planning of urban flood damage mitigation, enabling adaptation to evolving environmental and socio-economic challenges.