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

Linked Author(s): Reza Ahmadian, Man Yue Lam, Changbo Jiang

Keywords: Urban resilience Urban flooding Meta-heuristic algorithms Route Optimisation

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. Meta-heuristic algorithms, known for their adaptability and ability to be refined according to different hazard characteristics, have gained attention. In this study, the meta-heuristic algorithms are improved by combining the flood hazard rating (FHR) and distance to improve the Ant Colony Optimisation algorithm (ACO), Particle Swarm Optimisation (PSO) algorithm, Genetic Algorithm (GA), and Sparrow Search Algorithm (SSA). 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. Among the improved algorithms, the Improved Ant Colony Optimisation (IACO) algorithm generates the safest routes, the Improved Genetic Algorithm (IGA) generates the simplest routes, and the Improved Particle Swarm Optimisation (IPSO) generates the fastest routes. In contrast, the Improved Sparrow Search Algorithm (ISSA) generates the slowest routes. This study contributes to urban route planning and strengthens societal resilience to flood risks.

DOI:

Year: 2025