Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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Uncertainty Analysis for Parameters of Probability Distribution in Rainfall Frequency Analysis by Bayesian MCMC and Metropolis Hastings Algorithm

Bayesian MCMC 및 Metropolis Hastings 알고리즘을 이용한 강우빈도분석에서 확률분포의 매개변수에 대한 불확실성 해석

  • Seo, Young-Min (Department of Civil Engineering, Yeungnam University) ;
  • Park, Ki-Bum (Department of Railroad and Civil Engineering, Dongyang University)
  • Received : 2010.09.27
  • Accepted : 2011.01.13
  • Published : 2011.03.31
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Abstract

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

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References

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