Forecast of Tropical Cyclone Occurrences based on Fuzzy Logic Algorithm

Abstract

Most studies of the tropical cyclone (TC) forecasting are focused on the track and the wind radius forecasting, even though a tropical cyclone formation alerts also developed. This paper takes a different approach and explores and forecast the TC occurrences. This study presents the development of fuzzy logic (FL) models for predicting the occurrence of TC from five primary TC genesis parameters. These parameters are low-level relative vorticity (θ), horizontal wind of upper troposphere (u), sea surface temperature (SST), equivalent potential temperature (θe), and specific humidity (q). The FL model was developed by employing the trapezoidal and triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the TC genesis parameters data, with a daily calculation period from 1989 to 2018. The amount of TC genesis parameters when the cyclone occurred of the lowest, middle, and upper tercile reconstruction was used as a threshold to build FL model. The model satisfactorily simulated the occurrence of TC with comparable error measures. The result exhibits the accuracy at 0.75 (range: 0 to 1, perfect score: 1). The evidence shows that the result provides insights into the adequacy of FL methods for forecasting the TC occurrences.