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Joint occurrence probability analysis of typhoon-induced storm surges and rainstorms using trivariate Archimedean copulas

机译:利用三变量阿基米德系数法分析台风引发的风暴潮和暴雨的联合发生概率

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摘要

In this paper, the trivariate frequency analysis method for joint probability assessment of typhoon-induced storm surges and rainstorms is presented. The proposed method includes three major processes. The first process is to the find the best fitted marginal cumulative distributions for wind speeds (W), storm surges (S) and rainstorms (R), respectively. It is found that the Lognormal distribution is more suitable for storm surges and rainstorms, and Weibull distribution is more suitable for wind speeds. The second process is to estimate the joint probability distributions of W, S and R using four trivariate copulas, namely, Gumbel, Clayton, Frank and AMH. It is found that the joint probability can be best modeled by Gumbel copula. The last process is to calculate the conditional joint distributions of typhoon-induced storm surges and rainstorms which is useful for government agencies for improving disaster system management during typhoon events. An effective Particle Swarm Optimization (PSO) is proposed for to estimate the parameters of marginal cumulative distributions and copula functions. Analysis results based on the maximum likelihood method (MLM) and log-likelihood function (LLF) demonstrate the effectiveness of the PSO.
机译:本文提出了一种三变量频率分析方法,用于台风诱发的风暴潮和暴雨的联合概率评估。所提出的方法包括三个主要过程。第一个过程是分别找到最适合风速(W),风暴潮(S)和暴雨(R)的边际累积分布。发现对数正态分布更适合于风暴潮和暴雨,而威布尔分布更适合于风速。第二个过程是使用Gumbel,Clayton,Frank和AMH这四个三元变量,估计W,S和R的联合概率分布。研究发现,联合概率可以用Gumbel copula最好地建模。最后一个过程是计算台风引发的风暴潮和暴雨的条件联合分布,这对于政府机构在台风事件期间改善灾害系统的管理很有用。提出了一种有效的粒子群优化算法(PSO)来估计边际累积分布和copula函数的参数。基于最大似然法(MLM)和对数似然函数(LLF)的分析结果证明了PSO的有效性。

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