27 Mar 2024
 | 27 Mar 2024

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

Chuanfeng Liu, Yan Li, Wenjuan Li, Hao Qin, Lin Mu, Si Wang, Darong Liu, and Kai Zhou

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.

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Chuanfeng Liu, Yan Li, Wenjuan Li, Hao Qin, Lin Mu, Si Wang, Darong Liu, and Kai Zhou

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2280', Anonymous Referee #1, 01 May 2024
    • AC1: 'Reply on RC1', Chuanfeng Liu, 22 Jun 2024
  • RC2: 'Comment on egusphere-2023-2280', Anonymous Referee #2, 13 May 2024
    • AC2: 'Reply on RC2', Chuanfeng Liu, 22 Jun 2024
Chuanfeng Liu, Yan Li, Wenjuan Li, Hao Qin, Lin Mu, Si Wang, Darong Liu, and Kai Zhou
Chuanfeng Liu, Yan Li, Wenjuan Li, Hao Qin, Lin Mu, Si Wang, Darong Liu, and Kai Zhou


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Short summary
Storm surges pose a significant flooding risk to coastal areas. This research, taking China's Daya Bay Petrochemical Industrial Zone as a case study, addresses the dynamic nature of flooding events and the limitations of traditional evacuation plans for individuals with restricted real-time information. By combining the hydrological model and artificial intelligence, the method proves highly effective in optimizing evacuation routes, providing invaluable guidance during actual storm surges.