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On the extreme hydrologic events determinants by means of Beta-Singh-Maddala reparameterization.
Domma, Filippo; Condino, Francesca; Franceschi, Sara; De Luca, Davide Luciano; Biondi, Daniela.
Afiliación
  • Domma F; Department of Economics, Statistics and Finance "Giovanni Anania", University of Calabria, Arcavacata di Rende, CS, Italy.
  • Condino F; Department of Economics, Statistics and Finance "Giovanni Anania", University of Calabria, Arcavacata di Rende, CS, Italy. francesca.condino@unical.it.
  • Franceschi S; Department of Economics and Statistics, University of Siena, Siena, Italy.
  • De Luca DL; Department of Informatics, Modelling, Electronics and System Engineering, University of Calabria, Arcavacata di Rende, CS, Italy.
  • Biondi D; Department of Informatics, Modelling, Electronics and System Engineering, University of Calabria, Arcavacata di Rende, CS, Italy.
Sci Rep ; 12(1): 15537, 2022 09 15.
Article en En | MEDLINE | ID: mdl-36109545
In previous studies, beta-k distribution and distribution functions strongly related to that, have played important roles in representing extreme events. Among these distributions, the Beta-Singh-Maddala turned out to be adequate for modelling hydrological extreme events. Starting from this distribution, the aim of the paper is to express the model as a function of indexes of hydrological interest and simultaneously investigate on their dependence with a set of explanatory variables in such a way to explore on possible determinants of extreme hydrologic events. Finally, an application to a real hydrologic dataset is considered in order to show the potentiality of the proposed model in describing data and in understanding effects of covariates on frequently adopted hydrological indicators.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Hidrología Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
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Imprimir
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Hidrología Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido