Assessment of tropical cyclone disaster loss in Guangdong Province based on combined model. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Chen, Shihong; Tang, Danling; Liu, Xiaoqing; Chunhua, Hu
• Is Part Of: Geomatics, natural hazards & risk. Volume 9:Issue 1(2018)
• Page Start: 431
• Page End: 441
• Publication Date: 2018-01-01
• Subjects: TC disaster loss assessment -- GA–Elman -- SVR -- GRNN -- combined model -- Guangdong province; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2018.1447024
• Languages: English
• ISSNs: 1947-5705

Abstract

ABSTRACT: 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.