Tropical Cyclone Size Identification over the Western North Pacific Using Support Vector Machine and General Regression Neural Network

Knowledge about tropical cyclone (TC) size is essential for disaster prevention and mitigation strategies, but due to the limitations of observations, TC data from open ocean are scarce. In this paper, several models developed identify parameters, including radius maximum wind (RMW) radii 34 (R34), 50 (R50), 64 (R64) knot winds, using various machine learning algorithms based on infrared channel imagery geostationary meteorological satellites over Western North Pacific (WNP). Through evaluation verification, trained optimized support vector proposed RMW R34, while general regression neural network set up R50 R64.