Natural Disaster Risk Assessment Using Information Diffusion and Geographical Information System

  • Zhang Jiquan
  • Liu Xingpeng
  • Tong Zhijun
Part of the Intelligent Systems Reference Library book series (ISRL, volume 33)


With the social and economic development, the losses caused by natural disasters were more and more serious. Natural disaster assessment, management and research are the important field, developing direction and hotspot issues on disaster science and geo-science in resent year. However, because most of the natural disasters are a small sample of events, and uncertainty of natural disasters, so natural disaster risk assessment is particularly difficult based on historical data. Information diffusion theory is useful method for natural disaster risk assessment based on small sample even; it is a fuzzy approach to quantitative analysis of the probability of natural disaster risk. Therefore, the information diffusion theory has unique advantages in natural disaster risk assessment and management. This chapter presents a Geographical Information Systems (GIS) and information diffusion theory-based methodology for spatio-temporal risk assessment of natural disasters, taking grassland fire disasters in the Northern China as the case study. Firstly, we discuss connotation and forming mechanism of natural disaster risk, basic theory and framework of natural disaster risk assessment and management. Secondly, we introduce information diffusion theories and Geographical Information Systems (GIS) in the form of definitions, theorems and applications comprehensively and systemically. Finally, we give the case study on application of information diffusion theory and Geographical Information Systems (GIS) on grassland fire disasters in the grassland area of the Northern China. We employed information matrix to analyze and to quantify fuzzy relationship between the number of annual severe grassland fire disasters and annual burned area. We also evaluated the consequences of grassland fire disaster between 1991 and 2006 based on historical data from 12 Northern China provinces. The results show that the probabilities of annual grassland fire disasters and annual damage rates on different levels increase gradually from southwest to northeast across the Northern China. The annual burned area can be predicted effectively using the number of annual severe grassland fire disasters. The result shows reliability as tested by two-tailed Pearson correlation coefficient. This study contributes as a reference in decision making for prevention of grassland fire disaster and for stockbreeding sustainable development planning. The fuzzy relationship could provide information to make compensation plan for the disaster affected area.


Geographical Information System Natural Disaster Geographic Information System Disaster Risk Information Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Bankoff, G., Frerks, G., Hilhorst, D.: Mapping Vulnerability: Disasters, Development and People (2003)Google Scholar
  2. 2.
    Wisner, B., Blaikie, P., Cannon, T., Davis, I.: At Risk - Natural hazards, people’s vulnerability and disasters. Routledge, Wiltshire (2004)Google Scholar
  3. 3.
    Aronoff, S.: Geographic Information Systems: a Management Perspective. WDL Publications, Ottawa (1989)Google Scholar
  4. 4.
    Huang, C.F.: Principle of information diffusion. Fuzzy Sets and Systems 91, 69–90 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Huang, C.F., Moraga, C.: Extracting fuzzy if–rules by using the information matrix technique. Journal of Computer and System Sciences 70(1), 26–52 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Finney, M.A.: The challenge of quantitative risk analysis for wildland fire. Forest Ecology and Management 211, 97–108 (2005)CrossRefGoogle Scholar
  7. 7.
    Castro, F.X., Tudela, A., Sebastià, M.T.: Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain). Agricultural and Forest Meteorology 116, 49–59 (2003)CrossRefGoogle Scholar
  8. 8.
    Jaiswal, R.K., Mukherjee, S., Raju, K.D., Saxena, R.: Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation 4, 1–10 (2002)CrossRefGoogle Scholar
  9. 9.
    Yi, C., Huang, C.F., Pan, Y.Z.: Flood disaster risk analysis for Songhua river basin based on theory of information diffusion. In: 7th International Conference (ICCS 2007), part 3 (2007)Google Scholar
  10. 10.
    Huang, C.F., Inoue, H.: Benefit of soft risk map made by using information diffusion technique. In: International FLINS (Fuzzy Logic and Intelligent Technologies in Nuclear Science) Conference (2004)Google Scholar
  11. 11.
    Liu, J., Huang, C.F.: An information diffusion technique for fire risk analysis. Journal of Donghua University (Eng. Ed.) 21(3), 54–57 (2004)Google Scholar
  12. 12.
    Chen, J.N., Huang, H.K., Tian, F.Z., et al.: A selective Bayes classifier for classifying incomplete data based on gain ratio. Knowledge-Based Systems 21, 530–534 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhang Jiquan
    • 1
  • Liu Xingpeng
    • 1
  • Tong Zhijun
    • 1
  1. 1.Institute of Natural Disaster Research, College of Urban and Environmental SciencesNortheast Normal UniversityChangchunP.R. China

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