## 1 Introduction  
Reservoirs are a vital form of infrastructure for flood control, irrigation, water supply, and power generation. They play a crucial role in supporting both the livelihoods of individuals and the sustainable development of the national economy in China. According to the 2022 _National Water Resources Development Bulletin_, China has constructed 95,296 reservoirs with a combined storage capacity of 988.7 billion cubic meters. However, dam failures occur intermittently due to inherent design flaws, extreme weather events, and management errors [1, 2]. For instance, on 1 August 2018, the Sheyuegou Reservoir in Xinjiang suffered overtopping damage from a brief but intense rainfall event. The failure of the dam led to 28 fatalities and direct economic losses of CNY 175 million [3]. On 1 May 2020, a section of the Sardoba Reservoir dam in Uzbekistan collapsed. The flooding caused six deaths, environmental damage, and economic losses. Over 100,000 people from Uzbekistan and Kazakhstan were evacuated [4]. Another significant case was the August 1975 collapse of the Banqiao and Shimantan dams in China, which caused the deaths of an estimated approximately 171,000 people and left millions more displaced. This event remains one of the world's most catastrophic dam failures [5]. In all these cases, timely and well-coordinated emergency evacuation could have significantly reduced fatalities and minimized damage. However, existing disaster prevention and evacuation plans often fail to adapt to rapidly changing flood conditions. They also do not provide timely or effective evacuation guidance for affected populations. Therefore, it is essential to analyze and predict the evolution of floodwaters following dam failures. Additionally, evacuation route planning methods need to be developed to ensure the safe relocation of populations downstream. This would enhance emergency management capabilities in dam breach scenarios, ultimately reducing casualties and property losses. In the field of flood evacuation planning, numerous studies have employed various computational techniques, such as cellular automata models, and optimization algorithms, like the ant colony optimization (ACO) algorithm [6; 7; 8; 9; 10]. For example, [PERSON] and [PERSON] studied evacuation route choices for residents in the riverside Kampung areas of Indonesia, analyzing the influence of individual characteristics, route risks, and network structures on evacuation decisions [11]. They optimized evacuation paths using spatial syntax. [PERSON] et al. combined Geographic Information System (GIS) path analysis with multi-criteria decision analysis to identify safe alternative routes in the Sabah region during floods [12]. [PERSON] et al. introduced a method for shortest evacuation path planning based on cellular automata, using a high-precision two-dimensional hydrodynamic model to explore flood characteristics in floodplain areas [13]. [PERSON] et al. proposed a Reinforcement Routing optimization model for evacuation paths based on reinforcement learning [14]. This model considers traffic conditions, path risks, and route safety, offering new insights into emergency evacuation path optimization. These approaches have successfully simulated flood dynamics and evacuation strategies, but they often fall short of considering the dynamic nature of real-time flood evolution. The coupling of hydrodynamic models with evacuation routing is essential for improving evacuation efficacy, as it allows for the simulation of real-time changes in both flood conditions and infrastructure disruption, which this study addresses more comprehensively. Commonly used bionic intelligent algorithms for path optimization include ant colony algorithms, genetic algorithms, and [PERSON]'s algorithm [15; 16; 17; 18; 19]. [PERSON] et al. analyzed and simulated A* algorithms, genetic algorithms (GAs), and probabilistic roadmap methods (PRMs).