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The application of information diffusion technique in probabilistic analysis to grassland biological disasters risk

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Listed:
  • Hao, Lu
  • Yang, Li-Zhe
  • Gao, Jing-Min

Abstract

Biological disaster risk analysis is a complicated system. The incompleteness (gray areas), the non-clarity (fuzziness) and the uncertainty (randomness) of the data cause many difficulties that must be addressed with the risk assessment. In China, grasshopper and rodent disasters often occur in remote pastoral regions. This causes the monitored data of biological disaster to have a short series and span a large spatial and temporal scale. As available data are small sample in size, the use of risk assessment is often limited. The grassland biological disaster is a complex non-linear system. For the complex non-linear problems, effective conclusion can not be obtained from the accurate probability theory and mathematical statistics theory, but the fuzziness method may be a better method. In this paper, the one-dimension information diffusion technology adopted in evaluating the grassland biological disaster risk for the small statistical sample. The results show that: The information diffusion technology can make up for the information blank caused by the incompleteness of data, can change the single-valued samples into set-valued samples and excavate the internal law contained in the incomplete sample so as to achieve the aim of making full use of the information. It also can be seen that the diffusion results obtained under different starting control points or different interval step sizes have relatively good consistency and continuity. Based on such stability, a biological disaster risk forecast method can be derived, and the risk map using the reciprocals of different transcending probability values to demonstrate the regional differences on the same disaster level was also made by combining with GIS technology. Compared to other mature theories and technologies, the theory and method of fuzzy information optimization processing has its shortcomings especially in the selection of information diffusion function and information diffusion coefficient, and many improvements are needed.

Suggested Citation

  • Hao, Lu & Yang, Li-Zhe & Gao, Jing-Min, 2014. "The application of information diffusion technique in probabilistic analysis to grassland biological disasters risk," Ecological Modelling, Elsevier, vol. 272(C), pages 264-270.
  • Handle: RePEc:eee:ecomod:v:272:y:2014:i:c:p:264-270
    DOI: 10.1016/j.ecolmodel.2013.10.014
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    References listed on IDEAS

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    1. Lu Hao & Xiaoyu Zhang & Shoudong Liu, 2012. "Risk assessment to China’s agricultural drought disaster in county unit," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(2), pages 785-801, March.
    2. Denys Yemshanov & Frank H. Koch & Yakov Ben‐Haim & William D. Smith, 2010. "Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest," Risk Analysis, John Wiley & Sons, vol. 30(2), pages 261-276, February.
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    3. Liu, Xiaoyang & He, Daobing & Yang, Linfeng & Liu, Chao, 2019. "A novel negative feedback information dissemination model based on online social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 371-389.
    4. Cheng-Guo Wu & Yi-Ming Wei & Ju-Liang Jin & Qiang Huang & Yu-Liang Zhou & Li Liu, 2015. "Comprehensive evaluation of ice disaster risk of the Ningxia–Inner Mongolia Reach in the upper Yellow River," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 179-197, February.

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