Estimating storm surge intensity with Poisson bivariate maximum entropy distributions based on copulas

Tao, Shanshan; Dong, Sheng; Wang, Nannan; Soares, C. Guedes

doi:10.1007/s11069-013-0654-6

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…En sus inicios la estimación de parámetros se realizaba con la técnica de mínimos cuadrados, promedios pesados, momentos y recientemente se perfeccionó la teoría de máxima verosimilitud. Actualmente los algoritmos de optimización o la técnica de máxima entropía son sin duda los procedimientos de mayor precisión, incluso para las distribuciones de extremos máximos y mínimos en sus versiones mezcladas bivariadas (Chen y Singh, 2018;Montaseri et al, 2018;Ahn y Palmer, 2016;Tao et al, 2013). Adicionalmente a la selección del tipo de distribución a utilizar, queda perfeccionar la técnica adecuada para la estimación de parámetros.…”

Section: Introductionunclassified

“…In the beginning, the estimation of parameters was done with the technique of least squares, heavy means, moments and recently the theory of maximum likelihood was perfected. Currently optimization algorithms or the maximum entropy technique are undoubtedly the most accurate procedures, even for the distributions of maximum and minimum extremes in their mixed bivariate versions (Chen & Singh, 2018, Montaseri et al, 2018, Ahn & Palmer, 2016Tao et al, 2013). In addition to the selection of the type of distribution to be used, it is necessary to perfect the appropriate technique for parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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Búsqueda Armónica para optimizar la función Gumbel Mixta univariada

Molina-Aguilar

López

Aparicio

2018

Tecnol. cienc. agua

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Phenomena analyzed in the field of engineering with frequency analyses are usually extreme events such as floods and droughts. Such events frequently have as origin the simultaneous occurrence of two phenomena, and therefore are analyzed with two simultaneous probability distributions. Such is the case of the climatological and hydrometric series in Mexico which, due to its exposure to phenomena such as hurricanes, are analyzed with two-population probability distributions. The good use of these distributions depends on the correct estimation of their parameters. The application of a harmonic search meta-heuristic algorithm for the estimation of the parameters that optimize the univariate Gumbel Mixed function is presented. Annual maximum hydrometric data are used to compare the best-fit of the univariate distribution with traditional methodologies, such as maximum likelihood and the Rosenbrock algorithm. The results show that there is a decrease in the root mean square error and a fast convergence when a harmonic search algorithm is used. With the decrease of the error of fit, estimation of the design flows values is improved. The pseudocode of the algorithm for the implementation of the proposed methodology is presented.

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Section: Introductionunclassified

Section: Introductionmentioning

confidence: 99%

Búsqueda Armónica para optimizar la función Gumbel Mixta univariada

Molina-Aguilar

López

Aparicio

2018

Tecnol. cienc. agua

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“…Shiau et al [17] used the copula function to describe the joint distribution of depth and duration of rainfall, ultimately deriving a depth-duration-frequency model. Tao et al [18] selected the Poisson bivariate compound maximum entropy distribution to establish the joint distribution of extreme water level and wave height in a typhoon period. Wahl et al [19] ascertained the likelihood of compound events of storm surge and heavy precipitation for the coastal areas of the United States (US), and the results showed that the flood risk from compound events was higher in the Atlantic/Gulf coast.…”

Section: Introductionmentioning

confidence: 99%

Copula-Based Joint Probability Analysis of Compound Floods from Rainstorm and Typhoon Surge: A Case Study of Jiangsu Coastal Areas, China

Yuan

Xiong

2018

Sustainability

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Coastal areas are vulnerable to floods caused by rainstorms and typhoons. It is necessary to ascertain the risk of floods caused by both of these extreme weather events. A conceptual risk model is proposed to evaluate the rainstorm risk, typhoon surge risk, and the compound risk in the coastal areas of Jiangsu Province during the period of 1960-2012. The results of the model show that the typhoon surge risk in the study region is greater than the rainstorm risk. Three Archimedean copulas were used to fit the joint probability distributions of the compound events. The Frank copula and the Gumbel copula proved to be the best-fitting joint distribution function for the Huaibei plain district and the Lixiahe district, respectively. The probability of the extreme compound events not happening is less than 90% in the study region. This means that the flood risk is mainly subject to the encounter of a low-level rainstorm and a low-level typhoon surge. The study shows that the northern region of Jiangsu Province is more vulnerable to the compound risk, and that we should pay more attention to the floods caused by the compound events of rainstorm and typhoon surge.

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“…Recently, research on the application of multivariate extreme values (EVs) has increased. In these studies, several possible probability distribution functions have been used to characterize extreme sea level events: the Copula function (Salvadori et al, 2015;Corbella and Stretch, 2012;Michele et al, 2007;Salvadori et al, 2013;Tao et al, 2013), multivariate EV 35 function (Morton and Bowers, 1996;Coles and Tawn, 1994;Bhunya, et al, 2011), and MGPD function (which is a type of multivariate EV function) (Falk et al, 2004;Rootzé n and Tajvidi, 2006).…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of extreme storm surges in a semi-enclosed bay

Luo

Shi

Wang

2017

Preprint

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Abstract:The prediction of extreme storm surges is a critical task for coastal area protection. This study 10 examines extreme storm surges in Beibu Bay, a semi-enclosed bay in the South China Sea, and their joint probabilities. A method for the advanced prediction of the extreme storm surges is proposed using a multivariate extreme statistical method. We further present practical guidelines of the proposed multivariate analysis method, including guidelines for simulation. The simulation can be extended to multidimensional data to simplify computation, so the proposed approach can be 15 extended to use more points' data from the semi-enclosed bay for predicting extreme storm surges probabilities. A practical case study illustrates the application of the proposed techniques for extreme storm surges prediction. A comparison of the conditional probabilities obtained from observations and simulation data show that the proposed method is effective.

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Estimating storm surge intensity with Poisson bivariate maximum entropy distributions based on copulas

Cited by 21 publications

References 22 publications

Búsqueda Armónica para optimizar la función Gumbel Mixta univariada

Búsqueda Armónica para optimizar la función Gumbel Mixta univariada

Copula-Based Joint Probability Analysis of Compound Floods from Rainstorm and Typhoon Surge: A Case Study of Jiangsu Coastal Areas, China

Multivariate analysis of extreme storm surges in a semi-enclosed bay

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