On bandwidth selection for nonparametric estimation in flood frequency analysis

Quintela-del-Rı́o, Alejandro

doi:10.1002/hyp.7852

Cited by 6 publications

(13 citation statements)

References 51 publications

(67 reference statements)

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“…A study is also available in Anderson and Meerschaert (), who found that the monthly mean flow is quite seasonal and possesses a heavy‐tailed distribution. These data have been also used in nonparametric studies (Quintela‐Del‐Río, ). In this article, Salt River hydrological data are employed to examine the approaches on flow prediction and extreme value analysis.…”

Section: Hydrological Datamentioning

confidence: 99%

“…A different type of statistical model applied to hydrological data involves using nonparametric curve estimation methods, which does not require restrictive assumptions on the distribution of the population of interest. Several papers have applied nonparametric estimation methods to hydrological time series to carry out predictions as well as to perform extreme value analysis (Lall et al, 1993;Guo et al, 1996;Sharma et al, 1997;Kim and Heo, 2002;Wang et al, 2009;Quintela-Del-Río, 2011).…”

Section: Introductionmentioning

confidence: 99%

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River flow modelling using nonparametric functional data analysis

Quintela-del-Rı́o

Francisco‐Fernández

2017

J Flood Risk Management

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Time series and extreme value analyses are two statistical approaches usually applied to study hydrological data. Classical techniques, such as autoregressive integrated moving‐average models (in the case of mean flow predictions), and parametric generalised extreme value fits and nonparametric extreme value methods (in the case of extreme value theory) have been usually employed in this context. In this article, nonparametric functional data methods are used to perform mean monthly flow predictions and extreme value analysis, which are important for flood risk management. These are powerful tools that take advantage of both, the functional nature of the data under consideration and the flexibility of nonparametric methods, providing more reliable results. Therefore, they can be useful to prevent damage caused by floods and to reduce the likelihood and/or the impact of floods in a specific location. The nonparametric functional approaches are applied to flow samples of two rivers in the United States. In this way, monthly mean flow is predicted and flow quantiles in the extreme value framework are estimated using the proposed methods. Results show that the nonparametric functional techniques work satisfactorily, generally outperforming the behaviour of classical parametric and nonparametric estimators in both settings.

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Section: Hydrological Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

River flow modelling using nonparametric functional data analysis

Quintela-del-Rı́o

Francisco‐Fernández

2017

J Flood Risk Management

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“…These kind of proposals are normally well adapted to the more irregular shapes found in practice for hydrological variables. Applications of the CDF estimation in this setting are presented, for instance, in [ 1 , 5 , 6 ] and [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, underestimation of return leves could lead to increase damages and associated costs, with the consequent losses for insurance companies. CI estimation for quantiles was previously considered in, among others, [ 6 , 10 – 13 ]. Other types of uncertainty include the randomness of the natural process or the uncertainty depending on the choice of a particular model [ 10 ] and are excluded from this study.…”

Section: Introductionmentioning

confidence: 99%

Comparing Simultaneous and Pointwise Confidence Intervals for Hydrological Processes

Francisco‐Fernández

Quintela-del-Rı́o

2016

PLoS ONE

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Distribution function estimation of the random variable of river flow is an important problem in hydrology. This issue is directly related to quantile estimation, and consequently to return level prediction. The estimation process can be complemented with the construction of confidence intervals (CIs) to perform a probabilistic assessment of the different variables and/or estimated functions. In this work, several methods for constructing CIs using bootstrap techniques, and parametric and nonparametric procedures in the estimation process are studied and compared. In the case that the target is the joint estimation of a vector of values, some new corrections to obtain joint coverage probabilities closer to the corresponding nominal values are also presented. A comprehensive simulation study compares the different approaches, and the application of the different procedures to real data sets from four rivers in the United States and one in Spain complete the paper.

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“…The work addressed the issue of statistical discrimination when the sampling density is unknown. Following this, various investigators presented alternate theoretical views for estimating the form of sampling density in a nonparametric setting [e.g., Rosenblatt , ; Whittle , ; Parzen , ; Bartlett , ], among which kernel density estimators (kdes) gained recognition and have been used in many disciplines, including hydrology [e.g., Tarboton et al ., ; Kwon et al ., ; Quintela‐del‐Río , ]. Introductory material on kde can be found in Silverman [], Scott [], Wand and Jones [], and Tsybakov [].…”

Section: Introduction To Bivariate Kernel Density Estimatormentioning

confidence: 99%

Bivariate frequency analysis of floods using a diffusion based kernel density estimator

Santhosh

Srinivas

2013

Water Resour. Res.

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1] Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel.Citation: Santhosh, D., and V. V. Srinivas (2013), Bivariate frequency analysis of floods using a diffusion based kernel density estimator, Water Resour. Res., 49,[8328][8329][8330][8331][8332][8333][8334][8335][8336][8337][8338][8339][8340][8341][8342][8343]

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On bandwidth selection for nonparametric estimation in flood frequency analysis

Cited by 6 publications

References 51 publications

River flow modelling using nonparametric functional data analysis

River flow modelling using nonparametric functional data analysis

Comparing Simultaneous and Pointwise Confidence Intervals for Hydrological Processes

Bivariate frequency analysis of floods using a diffusion based kernel density estimator

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