A Diversity Driven Deep Convolutional Network for Enhanced Coastal Urban Flood Risk Assessment

Climate change and urbanization intensify urban pluvial flooding, posing significant threats to human lives and infrastructure. This situation underscores the critical need for efficient and accurate predictive systems for disaster prevention and mitigation. Traditional flood simulation models, while precise, are often limited by their data-intensive requirements and substantial computational complexity. In contrast, deep learning (DL) models show their advantages by high efficiency and powerful capability in processing large-scale non-linear data, making them highly appropriate for modeling complex flood dynamics. Consequently, integrating DL with conventional urban flood models has emerged as a promising strategy to enhance the accuracy and efficiency of flood prediction systems. However, existing research predominantly focuses on inland flooding, with limited attention to the role of tidal levels in coastal cities, which can significantly impact the accuracy of urban flood simulations.
To bridge the GAP, this study proposes an innovative hybrid DL approach that explores spatial and temporal data to improve the accuracy and efficiency of urban flood simulations, particularly in coastal areas. Simulation results from physics-based urban flood models are utilized to construct a comprehensive database for the DL model. Afterwards, patch-size and random sampling methods are employed to construct the sample dataset for training DL models. The convolutional neural network (CNN)-based data-driven urban pluvial flood model can simulate floods using topographic, rainfall, and tidal data, enabling the simulation of large urban areas within seconds. Incorporating diverse input data and advanced network architectures enhances model robustness and generalization across various scales and rainfall events. Fusion models that combine the strengths of DL and traditional hydrological models demonstrate improved prediction accuracy and computational efficiency by integrating tidal data and other environmental factors. Consequently, these hybrid models hold significant potential for integration into early warning systems and supporting decision-making processes in urban flood risk management.