Integration degree of risk in terms of scene and application

Risk is a scene in the future associated with some adverse incident. Scene means something seen by a viewer, or felt by individuals or various societal groups. Any risk assessment is to model some aspects of the scene for risk. Different aspects for assessment leads to different scene. In this paper, we suggest the integration degree of risk to distinguish characters of risks with respect to the aspects. The total number of factors of a risk system determines the macro degree and the granulation scale for measuring a risk reflects the micro degree. A simple framework depends on the degrees provides an explanation of the integrated risk. The most common model for risk assessment is available for the two-freedom-degree serial risk. A case studying flood risk shows the application to explain what the risk is, where the information is incomplete and we use the information diffusion technique to estimate the risk.

[1]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[2]  Huang Chong-fu.  An application of calculated fuzzy risk , 2002 .

[3]  J. Sekizawa,et al.  A comparison between integrated risk assessment and classical health/environmental assessment: emerging beneficial properties. , 2005, Toxicology and applied pharmacology.

[4]  F. Knight.  The economic nature of the firm: From Risk, Uncertainty, and Profit , 2009 .

[5]  Kurt Fedra,et al.  Integrated risk assessment and management: overview and state of the art , 1998 .

[6]  Robert J. Budnitz,et al.  Recommendations for probabilistic seismic hazard analysis: Guidance on uncertainty and use of experts , 1997 .

[7]  Cengiz Kahraman,et al.  Project risk evaluation using a fuzzy analytic hierarchy process: An application to information technology projects , 2006, Int. J. Intell. Syst..

[8]  Yong Shi,et al.  Towards Efficient Fuzzy Information Processing - Using the Principle of Information Diffusion , 2002, Studies in Fuzziness and Soft Computing.

[9]  Chongfu Huang.  Risk Analysis with Information Described in Natural Language , 2007, International Conference on Computational Science.

[10]  Manju Mohan,et al.  Integrated risk analysis for acute and chronic exposure to toxic chemicals. , 2003, Journal of hazardous materials.

[11]  C. Hardy.  Wildland fire hazard and risk: Problems, definitions, and context , 2005 .

[12]  R. Ruelas,et al.  A defuzzification method respecting the fuzzification , 1997, Fuzzy Sets Syst..

[13]  Keith Smith.  Environmental Hazards: Assessing Risk and Reducing Disaster , 1991 .

[14]  Glenn W. Suter II,et al.  Approaches for Integrated Risk Assessment , 2003 .

[15]  R. Neil Sampson,et al.  Application of hazard and risk analysis at the project level to assess ecologic impact , 2005 .

[16]  H Roberts,et al.  Risk Society: Towards a New Modernity , 1994 .

[17]  C Whipple,et al.  Risks of Risk Decisions , 1980, Science.

[18]  H. O. Wood,et al.  Modified Mercalli intensity scale of 1931 , 1931 .