A new information diffusion modelling technique based on vibrating string equation and its application in natural disaster risk assessment

In this paper, to naturally fill the gap in incomplete data, a new algorithm is proposed for estimating the risk of natural disasters based on the information diffusion theory and the equation of the vibrating string. Two experiments are performed with small samples to investigate its effectiveness. Furthermore, to demonstrate the practicality of the new algorithm, it is applied to study the relationship between epicentral intensity and earthquake magnitude, with strong-motion earthquake observations measured in Yunnan Province in China. The regression model, the back-propagation neural network and the conventional information diffusion model are also involved for comparison. All results show that the new algorithm, which can unravel fuzzy information in incomplete data, is better than the main existing methods for risk estimation.

[1]  Yangsheng You,et al.  THE THEORY OF OPTIMAL INFORMATION DIFFUSION ESTIMATION AND ITS APPLICATION , 2002 .

[2]  Chongfu Huang,et al.  Principle of information diffusion , 1997, Fuzzy Sets Syst..

[3]  Jun Guo,et al.  Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model , 2012, Expert Syst. Appl..

[4]  Sisi Zhao,et al.  A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[6]  Qiong Li,et al.  Research on flood risk analysis and evaluation method based on variable fuzzy sets and information diffusion , 2012 .

[7]  Pei-Zhuang Wang,et al.  A factor spaces approach to knowledge representation , 1990 .

[8]  Huang Chong-fu,et al.  Principle of information diffusion , 1997 .

[9]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[10]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[11]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[12]  Xuegong Xu,et al.  Risk assessment of soil erosion in different rainfall scenarios by RUSLE model coupled with Information Diffusion Model: A case study of Bohai Rim, China , 2013 .

[13]  Peng Peng,et al.  A risk assessment modeling technique based on knowledge extraction and information diffusion with support specification , 2013, Int. J. Gen. Syst..

[14]  Yim Ling Siu,et al.  Exceedance probability of multiple natural hazards: risk assessment in China’s Yangtze River Delta , 2013, Natural Hazards.

[15]  R. Quandt.  The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes , 1958 .

[16]  Huang Chongfu,et al.  INFORMATION MATRIX AND APPLICATION* , 2001 .

[17]  Claudio Moraga,et al.  A diffusion-neural-network for learning from small samples , 2004, Int. J. Approx. Reason..

[18]  Chongfu Huang,et al.  Information Diffusion Techniques and Small-Sample Problem , 2002, Int. J. Inf. Technol. Decis. Mak..

[19]  Hareton K. N. Leung,et al.  Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  Bradley P. Carlin,et al.  BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..