Artificial intelligence for hurricane storm surge hazard assessment

Abstract

Risk-informed coastal management requires assessment of extreme flood hazards from a large number of storm scenarios. To account for impact of climate change based on potential variations in greenhouse gas concentration and climate models, the number of storm scenarios would be even larger. Although physics-based hydrodynamic numerical models could predict flood levels and their impact from storm scenarios, the high computational cost of the solutions hinders the ability to perform the required number of simulations. Towards alleviating that cost, we show that physics-based simulations can be combined with Artificial Neural Network models to support more faster and effective prediction of low-probability events that account for uncertainties associated with climate change. We show this capability by predicting 10, 100, and 1,000 years return periods for peak storm surge height at a specific location on an idealized coastline. A large data set of synthetic tropical cyclones is generated from physics-based simulations and used for training, validating and testing the constructed neural network model. The ANN predicted values are validated against values from the physics-based simulations. The advantage of the combined approach is that, once the training was complete, it was performed in a fraction of the time required for the physics-based simulations.