Flood Double Frequency Analysis: 2D-Archimedean Copulas vs Bivariate Probability Distributions

Tsakiris, George; Kordalis, Nikos; Tsakiris, V.

doi:10.1007/s40710-015-0078-2

Cited by 21 publications

(5 citation statements)

References 16 publications

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“…The recurrence of extreme rainfall is calculated by probabilistically calculating the recurrence of these extreme events. The calculation of this extreme value was introduced by Gumbel [40] and many researchers have applied extreme value analysis to estimate the probabilities of extreme flood events, rainfall events [41][42][43][44][45][46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Changing of return periods of extreme rainfall using satellite observation in Java Island

Giarno,

Ruslana,

Rachmawardhani

et al. 2023

E3S Web Conf.

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As a flood-prone area and a centre of activity in Indonesia, the high intensity of rainfall needs to be watched out for. Excessive rainfall can lead to disastrous outcomes like floods and landslides. Analyzing the frequency of extreme rainfall events, calculated by a statistical approach involving employing probability distribution models and generalized extreme value distributions. Return levels are defined as the maximum value that will be exceeded once in a given time period. This study aims to map changes in maximum rainfall that have occurred repeatedly on the island of Java in the last 40 years. Changes in the rainfall return period are detected using CHIRPS rainfall estimates, by calculating every 20 years. The calculation results show high rainfall intensity in the south of Java Island for all return periods and there is an increase in rainfall in the north of Jakarta. The southern region of West Java and Central Java has also experienced a relatively high increase in rainfall intensity, especially over a 20-year period. Meanwhile in East Java Province, increased rainfall occurred in the easternmost part of the province.

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Section: Discussionmentioning

confidence: 99%

Changing of return periods of extreme rainfall using satellite observation in Java Island

Giarno,

Ruslana,

Rachmawardhani

et al. 2023

E3S Web Conf.

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“…Finally, the two-variate Archimedean copulas including Clayton, Frank and Gumbel, as well as the Gaussian copulas are utilized as candidate functions to determine the best-fit model for drought characteristics. The Archimedean copulas are broadly used in hydrological and water-resource analyses, due to their construction simplicity, strong representation and the possibility to be used even if the correlation between the variables is negative (Zhang and Singh, 2006;Tsakiris et al, 2015). Bayesian information criteria (BIC), root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) performance measures were used as goodness-of-fit tests to identify the most suitable function among the candidate copulas.…”

Section: Concept Of Copulamentioning

confidence: 99%

Bivariate Frequency Analysis of Hydrological Drought Using Copula: A Case Study of Northern Iraq

Hasan¹,

Abdullah²,

Awchi³

et al. 2023

JJCE

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In this research work, copula-based methodology is adopted to analyze the hydrological drought frequency. Standardized Runoff Index SRI was calculated using monthly-streamflow data for 50 years of two gauging stations in the northern region of Iraq. The drought duration and severity were extracted using run theory. Three Archimedean family and Gaussian copulas were used and compared to select the most appropriate copula model for bivariate frequency analysis of hydrological-drought characteristics. The dependence between drought duration and drought severity was estimated by Pearson's, Spearman's rho and Kendall's tau correlations. Various probability distributions were utilized to determine the best fit marginal distributions for drought characteristic variables based on the Kolmogorov-Smirnov and Chi-squared statistics. Uni-variate and joint return periods were estimated and compared. Generally, the results indicate that Archimedean copulas performed better than the Gaussian copulas. Exponential and Weibull distributions are the best fit for drought duration and severity, respectively, except for drought severity in case of the 9-month time scale at Lesser Zab region, where lognormal distribution was chosen. The current study can give helpful information for drought-risk assessment and water-resource management under climate change. KEYWORDS: Hydrological drought, Copula, SRI, Greater Zab river, Lesser Zab river, Bivariate return periods.

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“…, n) is a joint multivariate cumulative distribution of a r subset of marginals. There is an important family of copulas called Archimedean copulas, recently used to model various types of real life data such as: financial data [31][32][33][34], hydrological data [35,36], signals [37], wireless communication data [38] or biomedical data [39]. The Archimedean copula is introduced by a copula generator function ψ.…”

Section: Preliminariesmentioning

confidence: 99%

Introducing higher order correlations to marginals' subset of multivariate data by means of Archimedean copulas

Domino,

Glos

2018

Preprint

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In this paper, we present the algorithm that alters the subset of marginals of multivariate standard distributed data into such modelled by an Archimedean copula. Proposed algorithm leaves a correlation matrix almost unchanged, but introduces a higher order correlation into a subset of marginals. Our data transformation algorithm can be used to analyse whether particular machine learning algorithm, especially a dimensionality reduction one, utilises higher order correlations or not. We present an exemplary application on two features selection algorithms, mention that features selection is one of the approaches to dimensionality reduction. To measure higher order correlation, we use multivariate higher order cumulants, hence to utilises higher order correlations be to use the Joint Skewness Band Selection (JSBS) algorithm that uses third-order multivariate cumulant. We show the robust performance of the JSBS in contrary to the poor performance of the Maximum Ellipsoid Volume (MEV) algorithm that does not utilise such higher order correlations. With this result, we confirm the potential application of our data generation algorithm to analyse a performance of various dimensionality reduction algorithms.

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Flood Double Frequency Analysis: 2D-Archimedean Copulas vs Bivariate Probability Distributions

Cited by 21 publications

References 16 publications

Changing of return periods of extreme rainfall using satellite observation in Java Island

Changing of return periods of extreme rainfall using satellite observation in Java Island

Bivariate Frequency Analysis of Hydrological Drought Using Copula: A Case Study of Northern Iraq

Introducing higher order correlations to marginals' subset of multivariate data by means of Archimedean copulas

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