Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model

CORC > 南海海洋研究所 > 中国科学院南海海洋研究所 > 热带海洋环境国家重点实验室(LTO)

	Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model
	Chen, SH; Tang, DL; Liu, XQ; Hu, CH
刊名	GEOMATICS NATURAL HAZARDS & RISK
	2018
卷号	9 期号:1 页码:431-441
关键词	TC disaster loss assessment GA-Elman SVR GRNN combined model Guangdong province
通讯作者	200911837@oamail.gdufs.edu.cn
英文摘要	Tropical cyclone (TC) disaster loss assessment is an important and difficult problem in TC prevention and disaster mitigation. Few studies have focused on combined model in this area. This study introduced a new combination model method to predict TC disaster loss, taking Guangdong province as an example. We analysed and collected 67 TC data from 1993 to 2009, which had impact on Guangdong province, in which 60 were randomly for training data and another 7 were for testing data. We conducted three models - GA-Elman neural networks, support vector regression (SVR) and generalized regression neural networks (GRNN), and the root mean square error (RMSE) value we got are 5.05, 7.85 and 3.82, respectively. Then the three models are combined into a comprehensive evaluation model by model combination method. The RMSE of the test results is 3.30. The results show that the combined model is superior to one individual model and it is a more accurate and stable method.
学科主题	Geology; Meteorology & Atmospheric Sciences; Water Resources
内容类型	期刊论文
源URL	[http://ir.scsio.ac.cn/handle/344004/17105]
专题	南海海洋研究所_热带海洋环境国家重点实验室(LTO)
推荐引用方式 GB/T 7714	Chen, SH,Tang, DL,Liu, XQ,et al. Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model[J]. GEOMATICS NATURAL HAZARDS & RISK,2018,9(1):431-441.
APA	Chen, SH,Tang, DL,Liu, XQ,&Hu, CH.(2018).Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model.GEOMATICS NATURAL HAZARDS & RISK,9(1),431-441.
MLA	Chen, SH,et al."Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model".GEOMATICS NATURAL HAZARDS & RISK 9.1(2018):431-441.