资源环境科技发展态势分析平台

Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1D pipe/2D overland flow models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run‐times, they are often unsuitable for real‐time flood prediction at a street‐scale. This study explores the potential of a machine learning method, Random Forest (RF), to serve as a surrogate model for urban flood predictions. The surrogate model was trained to relate topographic and environmental features for 16,914 road segments from the coastal city of Norfolk, Virginia, USA to hourly water depths predicted by a high‐resolution 1D/2D physics‐based model. Two training scenarios for the Random Forest model were explored: (i) training on only the most flood‐prone street segments in the study area and (ii) training on all 16,914 street segments in the study area. The RF model yielded high predictive skill, especially for the scenario when the model was trained on only the most flood‐prone streets. The results also showed that the surrogate model reduced the computational run‐time of the physics‐based model by a factor of 3,000, making real‐time decision support feasible compared to using the full physics‐based model. We concluded that machine learning surrogate models strategically trained on high‐resolution and high‐fidelity physics‐based models have the potential to significantly advance the ability to support decision making in real‐time flood management within urban communities.