Functional and Multivariate Hydrological Data Visualization and outlier Detection of Sukkur Barrage

Hussain, Insia; Uddin, Mudassir

doi:10.5120/ijca2019919097

Cited by 4 publications

(8 citation statements)

References 24 publications

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“…Numerous studies conducted in hydrology using various nonparametric approaches. The functions based on depth have been recently employed for the detection of outliers by [14,15]. According to [16], nonparametric models are suitable for capturing subtle aspects related to the frequency estimation of a flood.…”

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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Section: Literature Reviewmentioning

confidence: 99%

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Hussain¹

2020

IJACSA

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“…The rainbow plot introduced by [1] and implemented by [2,4] in hydrology for the purpose of visualizing a complete set of employed data through a single plot having a distinct feature of displaying data curves using rainbow colour palette following data order. Two indices are used for data ordering that are functional depth and data density.…”

Section: ) Rainbow Plotmentioning

confidence: 99%

“…2) Functional bagplot: The bivariate bagplot based on half-space depth function was introduced by [26]. This plot was employed in multivariate and functional context by [27] and [2,4] respectively in hydrology using streamflow data. The functional bagplot is obtained following the pattern of bivariate bagplot which is composed using the first two score series of principal components in such a way that each bivariate bagplot point correspond to each functional bagplot curve.…”

Section: ) Rainbow Plotmentioning

confidence: 99%

“…The pattern employed for the formation of bivariate boxplot is followed for the composition of a functional boxplot. The detailed study regarding functional boxplot was organized by [1] and practised by [2,4] in hydrology. Recently, applications of boxplot in functional context have been conducted by [29,30].…”

Section: ) Rainbow Plotmentioning

confidence: 99%

“…The proposed graphical methods for data visualization and outlier detection were practised by [2][3][4] on hydrological data. Hence the work of [1,2] is intended to be implemented on a daily flow series of Taunsa Barrage on Indus river in Pakistan.…”

Section: Introductionmentioning

confidence: 99%

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Outlier Detection using Graphical and Nongraphical Functional Methods in Hydrology

Hussain¹

2019

IJACSA

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Graphical methods are intended to be introduced in hydrology for visualizing functional data and detecting outliers as smooth curves. These proposed methods comprise of a rainbow plot for visualization of data in large amount and bivariate and functional bagplot and boxplot for detection of outliers graphically. The bagplot and boxplot are composed by using first two score series of robust principal component following Tukey's depth and regions of highest density. These proposed methods have the tendency to produce not only the graphical display of hydrological data but also the detected outliers. These outliers are intended to be compared with outliers obtained from several other existing nongraphical methods of outlier detection in functional context so that the superiority of the proposed graphical methods for identifying outliers can be legitimated. Hence present paper aims to demonstrate that the graphical methods for detection of outliers are authentic and reliable approaches compare to those methods of outlier detection that are nongraphical.

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Review of Applicable Outlier Detection Methods to Treat Geomechanical Data

2023

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The reliability of geomechanical models and engineering designs depend heavily on high-quality data. In geomechanical projects, collecting and analyzing laboratory data is crucial in characterizing the mechanical properties of soils and rocks. However, insufficient lab data or underestimating data treatment can lead to unreliable data being used in the design stage, causing safety hazards, delays, or failures. Hence, detecting outliers or extreme values is significant for ensuring accurate geomechanical analysis. This study reviews and categorizes applicable outlier detection methods for geomechanical data into fence labeling methods and statistical tests. Using real geomechanical data, the applicability of these methods was examined based on four elements: data distribution, sensitivity to extreme values, sample size, and data skewness. The results indicated that statistical tests were less effective than fence labeling methods in detecting outliers in geomechanical data due to limitations in handling skewed data and small sample sizes. Thus, the best outlier detection method should consider this matter. Fence labeling methods, specifically, the medcouple boxplot and semi-interquartile range rule, were identified as the most accurate outlier detection methods for geomechanical data but may necessitate more advanced statistical techniques. Moreover, Tukey’s boxplot was found unsuitable for geomechanical data due to negative confidence intervals that conflicted with geomechanical principles.

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Functional and Multivariate Hydrological Data Visualization and outlier Detection of Sukkur Barrage

Cited by 4 publications

References 24 publications

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Outlier Detection using Graphical and Nongraphical Functional Methods in Hydrology

Review of Applicable Outlier Detection Methods to Treat Geomechanical Data

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