Modeling of rainfall variability using functional principal component method: a case study of Taiz region, Yemen

Hael, Mohanned Abduljabbar

doi:10.1007/s40808-020-00876-w

Cited by 17 publications

(8 citation statements)

References 38 publications

(33 reference statements)

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“…The study area is the Taiz region, which is considered one of the largest cities located in the southwest of Yemen at the geographical coordinates of 13˚34'46''N 44˚01'15''E [16]. Its average height from sea level is about 1311 m. The mean annual rainfall is approximately between 800 -1200 mm, while the mean monthly evaporation is almost 140 mm.…”

Section: An Application To Rainfall Datamentioning

confidence: 99%

Identifying Extreme Rainfall Events Using Functional Outliers Detection Methods

Hael¹,

Yuan²

2020

JDAIP

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Outlier detection techniques play a vital role in exploring unusual data of extreme events that have a critical effect considerably in the modeling and forecasting of functional data. The functional methods have an effective way of identifying outliers graphically, which might not be visible through the original data plot in classical analysis. This study's main objective is to detect the extreme rainfall events using functional outliers detection methods depending on the depth and density functions. In order to identify the unusual events of rainfall variation over long time intervals, this work conducts based on the average monthly rainfall of the Taiz region from 1998 to 2019. Data were extracted from the Tropical Rainfall Measuring Mission and the analysis has been processed by R software. The approaches applied in this study involve rainbow plots, functional highest density region box-plot as well as functional bag-plot. According to the current results, the functional density box-plot method has proven effective in detecting outlier compared to the functional depth bag-plot method. In conclusion, the results of the current study showed that the rainfall over the Taiz region during the last two decades was influenced by the extreme events of years 1999, 2004, 2005, and 2009.

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Section: An Application To Rainfall Datamentioning

confidence: 99%

Identifying Extreme Rainfall Events Using Functional Outliers Detection Methods

Hael¹,

Yuan²

2020

JDAIP

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“…Simulation of drought interval and drought changes were analysed by [29,30]. [31][32][33] studied rainfall variability modeling, pattern identification, and outlier detection. Other relevant studies include work of [34][35][36][37][38], are also preferred for acquiring information about the useful application of AFPC in hydrology.…”

Section: Literature Reviewmentioning

confidence: 99%

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Hussain¹

2020

IJACSA

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Several issues arise when extending the methods of outlier detection from a single dimension to a higher dimension. These issues include limited methods for visualization, marginal methods inadequacy, lacking a natural order and limitation in parametric modeling. The intension to overcome and address such limitations the nonparametric outlier identifier, based on depth functions, is introduced. These identifiers comprise of four threshold type outlyingness functions for outlier detection that are Mahalanobis distance, Tukey depth, spatial Mahalanobis depth, and projection depth. The object of the present research is the application of the proposed nonparametric technique in hydrology. The study is intended to be executed in two different frameworks that are multivariate hydrological data analysis and functional hydrological data analysis. The event of a flood is graphically represented by hydrograph whose components are used for computing flood characteristics that are peak(p) and volume(v). These characteristics are frequently employed for the various types of analysis in the multivariate study. Whereas, hydrograph is exhaustively employed in the analysis of functional data so that all the important information regarding flood event are not missed while analysis. The proposed technique in a multivariate framework is applied to the bivariate flood characteristics while in functional framework proposed approach is applied to the initial two scores of principal components denoted as , since initial two principal components capture major variation of data employed for analysis.

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“…Thereby, for articles that have dealt with stationarity of functional time series, see [2,15,17,29]. Another important subject of study is Functional Principal Component Analysis (FPCA), see [24,33], since FPCs, the eigenvalues and eigenfunctions of the covariance operator, yield an efficient representation.…”

Section: Introductionmentioning

confidence: 99%

Lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces

Kühnert¹

2022

Electron. J. Statist.

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A major task in Functional Time Series Analysis is measuring the dependence within and between processes for which lagged covariance and cross-covariance operators have proven to be a practical tool in wellestablished spaces. This article focuses on estimating these operators of processes in Cartesian products of abstract Hilbert spaces. We derive precise asymptotic results for the estimation errors for fixed and increasing lag and Cartesian powers under very mild conditions, presumably even under the mildest that can be assumed, establish estimators for the principal components, and conduct a simulation study.

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Modeling of rainfall variability using functional principal component method: a case study of Taiz region, Yemen

Cited by 17 publications

References 38 publications

Identifying Extreme Rainfall Events Using Functional Outliers Detection Methods

Identifying Extreme Rainfall Events Using Functional Outliers Detection Methods

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces

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