Lee R T, Liu J K
Polytechnic University, Kowloon, Hong Kong.
IEEE Trans Neural Netw. 2000;11(3):680-9. doi: 10.1109/72.846739.
In this paper, we present an automatic and integrated neural network-based tropical cyclone (TC) identification and track mining system. The proposed system consists of two main modules: 1) TC pattern identification system using neural oscillatory elastic graph matching model (NOEGM); and 2) TC track mining system using hybrid radial basis function (HRBF) network with time difference and structural learning (TDSL) algorithm.For system evaluation, 120 TC cases appeared in the period between 1985 and 1998 provided by National Oceanic and Atmospheric Administration (NOAA) are being used. In TC pattern recognition from satellite pictures, an overall 98% of correct TC pattern segmentation rate and over 97% of correct classification rate are attained. Moreover, for TC track and intensity mining test, promising result of over 86% is achieved with the application of the hybrid RBF network. Comparing with the bureau numerical TC prediction model (OTCM) used by Guam and the enhanced model (TFS) proposed by Jeng et al., the proposed hybrid RBF has attained an over 30% and 18% improvement in forecast errors.
在本文中，我们提出了一种基于神经网络的自动集成热带气旋（TC）识别与轨迹挖掘系统。该系统主要由两个模块组成：1）使用神经振荡弹性图匹配模型（NOEGM）的热带气旋模式识别系统；2）使用具有时差和结构学习（TDSL）算法的混合径向基函数（HRBF）网络的热带气旋轨迹挖掘系统。为了进行系统评估，我们使用了美国国家海洋和大气管理局（NOAA）提供的1985年至1998年期间出现的120个热带气旋案例。在从卫星图片进行热带气旋模式识别时，热带气旋模式分割的总体正确率达到98%，分类正确率超过97%。此外，在热带气旋轨迹和强度挖掘测试中，应用混合径向基函数网络取得了超过86%的良好结果。与关岛使用的局数值热带气旋预测模型（OTCM）和Jeng等人提出的增强模型（TFS）相比，所提出的混合径向基函数在预测误差方面分别提高了30%以上和18%。
