Investigation with Kendall plots of infiltration index–maximum rainfall intensity relationship for regionalization

Gargouri-Ellouze, Emna; Bargaoui, Zoubeïda

doi:10.1016/j.pce.2009.02.001

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…It is worth noting that the catchments in a same cluster are not necessarily geographically contiguous; in effect, the geographical proximity of the catchments is not a guarantee of their hydrological similarity [45]. This result is in accordance with the one described by Gargouri-Ellouze and Bargaoui [41].…”

Section: Resultssupporting

confidence: 78%

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Chérif

Gargouri-Ellouze

2023

Preprint

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Partitioning methods such as cluster analysis are advantageous in pooling catchments into hydrometric similar regions. However, these study cases are always infrequent in Sud Mediterranean zones and remain under-represented in international publications. This paper illustrates a Tunisian application case, which aims to pool catchments with hierarchical clustering method based on distances calculated in multidimensional physiographical and hydrometric space. Homogeneity of generated clusters is checked by Silhouette index. Current study considers nineteen Tunisian catchments, in a semi-arid climate observed since 1992. Areas and annual average rainfall respectively vary in [1–10 km2 ] and [280–500 mm] ranges. Twelve physiographical attributes and nine rainfall and streamflow signatures are considered in hierarchical partitioning procedure with two clusters. Correlation distance provides the most homogeneous clusters. Statistics demonstrate that: percentage of area affected by anti-erosive practices, percentage of forest cover and catchment’s area are the most discriminant attributes. However, hydrometrical signatures appear to be not relevant. This partitioning highlight two different hydrological behaviors which must be in support of forecasting. Results are promising as a Sud Mediterranean case, where the shortage of hydrometrical data is an occurring problem. They have the advantageous of enabling hydrologic forecasting without need of heavy information.

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Section: Resultssupporting

confidence: 78%

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Chérif

Gargouri-Ellouze

2023

Preprint

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“…Various publications have proposed and studied different methods to measure a distance between the distributions H(x, y) and F (x)G(y) to quantify dependence for the pair (X, Y ) (e.g., Balakrishnan & Lai, 2009;Reshef et al, 2011;Vexler et al, 2014). Graphical display tools to evaluate a rich class of bivariate dependence structures (Fisher & Switzer, 1985Bjerve & Doksum, 1993;Jones, 1996;Jones & Koch, 2003) include a K-plot procedure (Genest & Boies, 2003;Gargouri-Ellouze & Bargaoui, 2009). The K-plot suggests to plot the Kendall distribution function K(t) = Pr{H(X, Y ) < t} against the function Pr{F (X)G(Y ) < t | X, Y are independent}.…”

Section: Introductionmentioning

confidence: 99%

Multi-Panel Kendall plot in light of an ROC curve analysis applied to measuring dependence

2018

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The Kendall plot (K-plot) is a plot measuring dependence between the components of a bivariate random variable. The K-plot graphs the Kendall distribution function against the distribution function of V U , where V and U are independent uniform [0, 1] random variables. We associate K-plots with the receiver operating characteristic (ROC) curve, a well-accepted graphical tool in biostatistics for evaluating the ability of a biomarker to discriminate between two populations. The most commonly used global index of diagnostic accuracy of biomarkers is the area under the ROC curve (AUC). In parallel with the AUC, we propose a novel strategy to measure association between random variables from a continuous bivariate distribution. First, we discuss why the area under the conventional Kendall curve (AUK) cannot be used as an index of dependence. We then suggest a simple and meaningful extension of the definition of the K-plots, and define an index of dependence that is based on AUK. This measure characterizes a wide range of two-variable relationships, thereby completely detecting the underlying dependence structure.Properties of the proposed index satisfy the mathematical definition of a measure. Finally, simulations and real data examples illustrate the applicability of the proposed method.

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“…The former was initially proposed by [4] and the latter was proposed by [5]. Some applications of these methods can be found in [6] where the relationship between oil price variation and stock indices is measured, in [7] where the relationship between storm characteristics are analyzed, and in [8] where the dependence between the infiltration index and the maximum rainfall intensity in an hydrological application.…”

Section: Introductionmentioning

confidence: 99%

Graphical Methods For Detecting Dependence

Guarín-Escudero¹,

Jaramillo-Elorza

Gómez³

2018

Cienc. En Desarro.

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Copulas have become a useful tool for modeling data when the dependence among random variables exists and the multivariate normality assumption is not fulfilled. The copulas have been applied in several fields. In finance, copulas are used in asset modeling and risk management. In biomedical studies, copulas are used to model correlated lifetimes and competitive risks [1]. In engineering, copulas are used in multivariate process control and hydrological modeling [2]. The interest in modeling multivariate problems involving dependent variables is generalized in several areas, making this methodology in a convenient way to model the dependence structure of random variables. However, in practice a first step before modeling phenomena through copulas is to assess whether there is dependence among the variables involved. In this paper some graphical methods to detect dependence are discussed and their performance will be evaluated through a simulation study. An application of graphical methods presented to insurance data is illustrated.

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Investigation with Kendall plots of infiltration index–maximum rainfall intensity relationship for regionalization

Cited by 11 publications

References 33 publications

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Flow signatures and catchment’s attributes for HCA clustering in a hydrologic similarity assessment (Tunisian case)

Multi-Panel Kendall plot in light of an ROC curve analysis applied to measuring dependence

Graphical Methods For Detecting Dependence

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