Effective Storm Surge Evacuation Planning Coupling Risk Assessment and DRL: A Case Study of Daya Bay Petrochemical Industrial Zone

Abstract. Storm surge is one of the most destructive marine disasters, characterized by abnormal and temporary rises in water levels during intense storms, leading to extreme inland flooding in the coastal area. Emergency evacuation planning, based on storm surge risk assessments, plays a crucial role in saving lives and mitigating disasters. Conventional emergency evacuation plans primarily adopt the perspective of administrators, providing evacuees with complete environmental information. However, in practical situations, evacuees often lack access to complete environmental information and need to select appropriate paths based on their limited awareness of their surroundings. This study coupled a risk assessment of storm surges with a road network to optimize evacuation routes in the Daya Bay Petrochemical Industrial Zone, a low-lying coastal region of Huizhou City, China, which is frequently affected by storm surge-driven flooding. A combination of the Deep Q-Network (DQN) model and raster environment was employed to develop real-time evacuation plans based on limited surrounding environments during storm surge events. To address the DQN model's convergence challenges, masked state space, masked action space, and tri-aspect reward mechanism were proposed, profoundly enhancing the model's convergence capabilities. The coupled ADCIRC-SWAN model and the Jelesnianski hurricane model were utilized to simulate storm surges for risk assessments under various typhoon scenarios. Additionally, potential safe shelters were identified to offer alternative evacuation options. Two distinct storm surge scenarios were employed as test environments, evaluating path plans for 1000 randomly selected starting points in each case. The results indicate that the proposed method is highly effective in devising optimal evacuation routes with minimal deviation, offering valuable guidance for evacuees during real-world storm surges.