Incremental Gaussian granular fuzzy modeling applied to hurricane track forecasting

This paper presents a Gaussian fuzzy set-based evolving modeling method, FBeM-G, to predict tropical cyclone tracks 6 hours in advance. FBeM-G summarizes similar data into Gaussian granules evolved from a sequence of data. It uses a recursive learning algorithm to update its parameters and structure over time and therefore is able to cope with nonstationarities. Past values of latitude, longitude, maximum sustained wind, pressure and wind radii in different quadrants of the Katrina, Sandy and Wilma tropical cyclones were obtained from the 'best track' analysis provided by the National Hurricane Center (NOAA). An ensemble of cloud-based and fuzzy models was considered to compare the estimated tracks. FBeM-G provided more accurate 6-hourly track estimates using a smaller number of local models and parameters. Although less accurate, longer-term estimates given by the ensemble approach became closer to those provided by FBeM-G. An outer approximation of the pointwise track prediction is a particular characteristic of the method that is useful to determine risk areas and actions to be taken.