灾害学2016，Vol.31Issue(3)：20-25,30,7.DOI:10.3969/j.issn.1000-811X.2016.03.004
基于等距特征映射降维的台风灾情概率神经网络预评估模型
Probabilistic Neural Network Pre-Assessment Model Based on Isometric Feature Mapping Dimentional Reduction in Typhoon Disaster
摘要
Abstract
Typhoon hazard,between hazard bearing body and the disaster is a complex nonlinear dynamical system;accurately and efficiently extract the important indicators for the pre-assessment of typhoon disaster grade is an important basis for disaster prevention and relief work.In this paper,we apply principal component analysis,i-sometric feature mapping and entropy to extract key indicators of hazard bearing body,with hazard source as the in-put neurons,and disaster grade as output neurons,establishing probabilistic neural network pre-assessment model in typhoon disaster.The results show that the accuracy of probabilistic neural network pre-assessment model based on the non-linear feature extraction isometric feature mapping reaches 90%,the model has a satisfactory level of accuracy and generalization ability,provide a new way for natural disaster risk assessment,having certain reference value.关键词
概率神经网络/等距特征映射/信息熵/台风/灾害/预评估Key words
probabilistic neural network/Isometric Feature Mapping/entropy/typhoon/disaster/pre-as-sessment分类
环境科学引用本文复制引用
陈燕璇,刘合香,谭金凯..基于等距特征映射降维的台风灾情概率神经网络预评估模型[J].灾害学,2016,31(3):20-25,30,7.基金项目
国家自然科学基金（41465003）；广西研究生教育创新计划项目 （）
