Spatiotemporal Deep Learning to Forecast Storm Surge Water Levels and Storm Trajectory: Case Study Hurricane Harvey

Abstract

Using Hurricane Harvey as a case study, this paper uses the hurricane track, wind velocity and pressure, bathymetry, Manning’s n coefficients, tidal forcing, and storm surge results generated by the ADCIRC+SWAN model as input to construct a uniform spatiotemporal deep learning model for storm surge forecasting. The model transforms inputs into embeddings and performs feature fusion and extraction. The regression layer of the model outputs the predicted values of storm surge water elevation, station water level time series, and hurricane tracks with attributes. To analyze the model’s adaptability and robustness as a surrogate model to ADCIRC, ablation experiments are conducted on up to 10 input variables to investigate the impact of various inputs on the results. Heat maps between 3, 6, 9, and 12 h horizon prediction and targets revealed excellent performance for the large scale of nodes and multiple inputs on the training set, validation set, and test set as the surrogate model. When the model is used to forecast water levels of 12 observation stations, the 9 h forecasting horizon is generally equal to or better than the ADCIRC simulation results. When the model is used to predict hurricane tracks and attributes, the 12 h forecast horizon is relatively close to the observed values, achieving satisfactory results. This model is developed and tested using Hurricane Harvey data and storm surge results as a case study. To develop a generalized prediction model would require a large amount of data and storm surge results from many hurricanes.