Abstract: 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.

Insia Hussain, “Outlier Detection using Graphical and Nongraphical Functional Methods in Hydrology” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101259

@article{Hussain2019,
title = {Outlier Detection using Graphical and Nongraphical Functional Methods in Hydrology},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0101259},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101259},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Insia Hussain},
}