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Across coastal urban centres, underground spaces such as storage areas, transportation corridors, basement car parks, public facilities, retail & office and private spaces present a priority risk during flood events with respect to timely evacuation. However, these underground spaces are commonly not considered in urban flood prediction models, in many cases because the location and geometry of these underground spaces are often poorly known. In order to improve urban flood prediction models, various identified underground spaces have been included into the urban flood simulation presented in this paper. Here, the Software MIKE+ is adopted to simulate the coastal flood scenarios for the urban centre of the city of Belfast, Northern Ireland. In the simulation, unstructured triangular grids are used. Based on the numerical simulation, urban flood depth and flooding rates into the underground spaces can be obtained. Based on the comparison of simulated urban flood scenarios with and without underground spaces, the impact of underground spaces on street-level inundation and flood routing is evaluated. It can be observed that the inclusion of underground space has a significant impact on the flood routing process. Moreover, the underground spaces also present priority risk areas during flood events with respect to timely evacuation and to this end, underground spaces cannot be ignored in real urban flood prediction. The presented study can be used to increase communities’ emergency preparedness and flood resilience
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Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community
Abstract Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D 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 to hourly water depths simulated by a high‐resolution 1‐D/2‐D physics‐based model at 16,914 road segments in the coastal city of Norfolk, Virginia, USA. Two training scenarios for the RF 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 more 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.
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- Award ID(s):
- 1735587
- PAR ID:
- 10448003
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 56
- Issue:
- 10
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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