Vol.1, No.2, 2019, pp.69-88, doi:10.32604/jai.2019.06535
Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons: A Combined PCA and BP Neural Network Model
  • Shuai Yuan1, Guizhi Wang1,*, Jibo Chen1, Wei Guo2
1 School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 Department of Mathematics & Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA.
* Corresponding Author: Guizhi Wang. Email: wgz@nuist.edu.cn.
This paper develops a joint model utilizing the principal component analysis (PCA) and the back propagation (BP) neural network model optimized by the Levenberg Marquardt (LM) algorithm, and as an application of the joint model to investigate the damages caused by typhoons for a coastal province, Fujian Province, China in 2005-2015 (latest). First, the PCA is applied to analyze comprehensively the relationship between hazard factors, hazard bearing factors and disaster factors. Then five integrated indices, overall disaster level, typhoon intensity, damaged condition of houses, medical rescue and self-rescue capability, are extracted through the PCA; Finally, the BP neural network model, which takes the principal component scores as input and is optimized by the LM algorithm, is implemented to forecast the comprehensive loss of typhoons. It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015, with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases. The model was validated using three typhoon events and it is found that the error is less than 1%. These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’ self-rescue capability.
Typhoon, PCA, BP neural network model, comprehensive loss, LM algorithm.
Cite This Article
. , "Assessing the forecasting of comprehensive loss incurred by typhoons: a combined pca and bp neural network model," Journal on Artificial Intelligence, vol. 1, no.2, pp. 69–88, 2019.
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