Abstract
Storm surge hazard annually costs hundreds of lives and billions of dollars for damages in coastal countries. Simulating the storm surge events and alerting flood areas in advance is essential to guarantee the safety of citizens and urban infrastructure. However, due to the complexity of the urban environment, the accuracy of simulation faces challenges. By leveraging the facility of the Internet-of-Things (IoT) technique, the urban flood simulation procedure under storm surge hazard can be improved. In this article, targeting a coastal city usually under the threat of the storm surge, a deep-reinforcement-learning-based storm surge flood simulation approach is proposed to simulate the flood situation under the specific level of the storm surge. By analyzing real-time urban flood data and weather data collected by the IoT system, the model of the urban flood can be constructed and refined. A case study based on storm surge took place on July 1, 2019 in Shenzhen and was conducted to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed method can effectively simulate the storm surge flood, and the achieved accuracy is up to 97.22%.
Published Version
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