Author(s): Chuannan Li; Changbo Jiang; Man Yue Lam; Reza Ahmadian

Linked Author(s):

Keywords: Urban resilience; Urban flooding; Metaheuristic algorithms; Route Optimisation; Flood Hazard Rating

Abstract: Floods cause severe damage to urban areas, and evacuation is an effective measure to enhance urban resilience. However, limited research focuses on optimising evacuation routes based on flood hazard characteristics. Metaheuristic algorithms, known for their adaptability and ability to be refined according to different hazard characteristics, have gained attention. In this study, the metaheuristic algorithms are improved by combining the flood hazard rating (FHR) and distance to improve the Ant Colony Optimisation algorithm (ACO) and Particle Swarm Optimisation (PSO) algorithm. The research framework includes flood modelling, flood hazard rating, and evacuation route generation, which were applied to the York flood in the UK. The results show that the improved algorithms can effectively avoid high-risk flood areas and optimise evacuation routes by their algorithm rules. Among the improved algorithms, the Improved Ant Colony Optimisation (IACO) algorithm generates the safest routes and the Improved Particle Swarm Optimisation (IPSO) generates the fastest routes. The improved metaheuristic algorithm introduced in this study presents an innovative method for optimising flood disaster routing, demonstrating potential possibility in flood scenarios application. This study contributes to urban route planning and strengthens societal resilience to flood risks.

DOI: https://doi.org/10.64697/978-90-835589-7-4_41WC-P1957-cd

Year: 2025