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Environmental risk modelling under probability-normal interval-valued fuzzy number

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Fuzzy Information and Engineering

Abstract

In almost all the realistic circumstances, such as health risk assessment and uncertainty analysis of atmospheric dispersion, it is very essential to include all the information into modelling. The parameters associated to a particular model may include different kind of variability, imprecision and uncertainty. More often, it is seen that available informations are interpreted in probabilistic sense. Probability theory is a well-established theory to measure such kind of variability. However, not all of available information, data or model parameters affected by variability, imprecision and uncertainty can be handled by traditional probability theory. Uncertainty or imprecision may occur due to incomplete information or data, measurement errors or data obtained from expert judgement or subjective interpretation of available data or information. Thus, model parameters, data may be affected by subjective uncertainty. Traditional probability theory is inappropriate to represent them. Possibility theory and fuzzy set theory is another branch of mathematics which is used as a tool to describe the parameters with insufficient or vague knowledge. In this paper, an attempt has been made to combine probability knowledge and possibility knowledge and draw the uncertainty. The paper describes an algorithm for combining probability distribution and interval-valued fuzzy number and applied to environmental risk modelling with a case study. The primary aim of this paper is to propagate the proposed method. Computer codes are prepared for the proposed method using MATLAB.

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Authors and Affiliations

  1. Department of Mathematics, Gauhati University, Guwahati, 781014, Assam, India

    Rituparna Chutia

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  1. Rituparna Chutia
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Correspondence to Rituparna Chutia.

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Chutia, R. Environmental risk modelling under probability-normal interval-valued fuzzy number. Fuzzy Inf. Eng. 5, 359–371 (2013). https://doi.org/10.1007/s12543-013-0150-4

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  • DOI: https://doi.org/10.1007/s12543-013-0150-4

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