GEOGRAPHICAL RESEARCH ›› 2009, Vol. 28 ›› Issue (5): 1243-1254.doi: 10.11821/yj2009050011

• Climate and Global Change • Previous Articles     Next Articles

Economic loss assessment of typhoon based on principal component analysis and neural network

LOU Wei-ping1, CHEN Hai-yan2, ZHENG Feng3, WU Rui1   

  1. 1. Xinchang County Meteorological Bureau, Xinchang 312500, Zhejiang, China;
    2. Zhejiang Provincial Meteorological Office, Hangzhou 310017, China;
    3. Wenzhou City Meteorological Bureau, Wenzhou 312500, Zhejiang, China
  • Received:2008-10-21 Revised:2009-05-15 Online:2009-09-25 Published:2009-09-25
  • Supported by:

    国家气象局新技术推广项目(CMATG2008M40)

Abstract:

The assessment model of direct economic losses from typhoon disaster in Zhejiang Province is established in this research. The data of direct economic losses in the study region are converted into direct economic losses indexes. Using principal component analysis method, the assessment factors representing disaster inducing factor, disaster-formative environment and disaster-affected body are processed, and the principal component is abstracted as the input of the BP neural network model, thus the assessment model is established. Historical fitting results are consistent with the reality. It is found in the actual assessments of five typhoons affecting Zhejiang in 2007 and 2008 that the post-disaster assessment values of typhoons are higher than the actual situations, and the severer impacts the storms have, the narrower the gap between the assessment values and the actual situation is, which reflects the impact of the disaster prevention and alleviation efforts against typhoons of great influence. According to the forecast values of wind and precipitation when the typhoon began to exert some affect, pre-assessments are conducted and the consequence shows that the pre-assessment results with relatively accurate forecast values are in accordance with the post-disaster assessment values, while the ones with less accurate forecast values are unsatisfactory. Therefore, this model can be applied in the actual assessment of direct economic loss from typhoon damage, and the accurate forecast of wind and precipitation before the typhoons have effect is crucial to the improvement of the accuracy of pre-assessments.

Key words: typhoon, direct economic losses, principal component analysis, BP neural networks, assessment