An efficient artificial intelligence model for prediction of tropical storm surge

Process-based models have been widely used for storm surge predictions, but
their high computational demand is a major drawback in some applications such as rapid
forecasting. Few efforts have been made to employ previous databases of synthetic/real
storms and provide more efficient surge predictions (e.g. using storm similarity of an
individual storm to those in the database). Here, we develop an alternative efficient and
robust artificial intelligent model, which predicts the peak storm surge using the tropical
storm parameters: central pressure, radius to maximum winds, forward velocity, and storm
track. The US Army Corp of Engineers, North Atlantic Comprehensive Coastal Study, has
recently performed numerical simulations of 1050 synthetic tropical storms, which statistically
represent tropical storms, using a coupled high resolution wave–surge modeling
system for the east coast of the US, from Cape Hatteras to the Canadian border. This study
has provided an unprecedented dataset which can be used to train artificial intelligence
models for surge prediction in those areas. While numerical simulation of a storm surge at
this scale and resolution (over 6 million elements scaling from 20 m to more than 100 km)
is extremely expensive, the artificial intelligence takes the advantage of the previous