Advanced Machine Learning Methods for Major Hurricane Forecasting
Javier Martínez-Amaya, Cristina Radin, Verònica Nieves
Abstract
Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new framework based on automated decision tree analysis, which has the capability to identify the most important cloud structural parameters from GOES imagery as predictors for hurricane intensification potential in the Atlantic and Pacific oceans. The proposed framework has been proved effective for predicting major hurricanes with an overall accuracy of 73% from 6 to 54 h in advance (both regions combined).