Predicting combined tidal and pluvial flood inundation using a machine learning surrogate model

Abstract

Flooding increases in recent years, in particular for coastal communities facing sea level rise, have brought renewed attention to real-time, street-scale flood forecasting. Such flood models using conventional physics-based modeling approaches are often unrealistic for real-time decision support use cases due to their long model runtime. Machine learning offers an alternative strategy whereby a surrogate model can be trained to mimic relationships present within the physics-based model and, after training, can run in seconds rather than hours. This study used the Random Forest (RF) algorithm to emulate a 1D/2D physics-based model simulating surface water depths in an urban coastal watershed in Norfolk, Virginia. Environmental features from a selected set of pluvial and tidal flood events and topographic information of the roadway were the input variables to train the surrogate model. Results show the potential for the surrogate model to predict flood extent and depth for both pluvial and tidal flood events. Furthermore, the surrogate model can differentiate between flooding locations dominated by pluvial or tidal flooding or impacted by both flooding mechanisms. Flood reports from the mobile app Waze were used for model validation and show 90% agreement with flooding locations from the surrogate model. Finally, feature importance methods were investigated to interpret the performance of the RF models and understand the contribution of different physical features to localized flooding.