Raymond S. T. Lee and James N. K. Liu, Hong Kong Polytechnic University
Severe weather prediction, such as tropical cyclone (TC) forecast is a typical data mining and forecasting problem that involves high level data manipulation and interpretation of meteorological information such as satellite pictures and other meteorological observation data. In this paper, we present a fully automatic and integrated system known as "ATOMOSPHER" - Automatic Track Mining and Object Satellite Pattern Hunting system using Enhanced RBF and EGDLM - to provide a neural network based TC identification and tracking system. The proposed system consists of two main modules: 1) Object Dvorak technique for TC satellite pattern identification based on an Elastic Graph Dynamic Link Model (EGDLM) and 2) TC tracking system based on an Enhanced Radial Basis Function (RBF) network model. For system evaluation, 120 TC cases appeared in the period from 1985 to 1998 (provided by National Oceanic and Atmospheric Administration (NOAA)) are adopted. Promising results of over 87% of TC pattern segmentation and 97% of correct classification rate are attained respectively. For TC tracking, an overall of over 86% correct prediction result is achieved.