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- W2896065737 abstract "This study generalized the best copula to characterize the joint probability distribution between rainfall severity and duration in Peninsular Malaysia using two dimensional copulas. Specifically, to construct copulas, Inference Function for Margins (IFM) and Canonical Maximum Likelihood (CML) methods were specially exploited. For the purpose of achieving copula fitting, the derived rainfall variables by making use of the Standardized Precipitation Index (SPI) were fitted into several distributions. Five copulas, namely Gaussian, Clayton, Frank, Joe and Gumbel were put to the tests to establish the best data fitted copula. The tests produced acknowledged and satisfactory results of copula fitting for rainfall severity and duration. Surveying the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), only three copulas produced a better fit for parametric and semi parametric approaches. Finally, two consistency tests were conducted and the results shown that Frank Copula produced consistent results." @default.
- W2896065737 created "2018-10-26" @default.
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- W2896065737 date "2019-02-04" @default.
- W2896065737 modified "2023-09-25" @default.
- W2896065737 title "A generalized bivariate copula for flood analysis in Peninsular Malaysia" @default.
- W2896065737 doi "https://doi.org/10.11113/mjfas.v15n2019.1275" @default.
- W2896065737 hasPublicationYear "2019" @default.
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