Classification of daily torrential rainfall patterns based on a robust correlation measure

Shazlyn Milleana Shaharudin

QR Code Link :
Type :	article
Subject :	G Geography (General)
Main Author :	Shazlyn Milleana Shaharudin
Title :	Classification of daily torrential rainfall patterns based on a robust correlation measure

Place of Production :	Tanjong Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

The objective of this study is to identify the main spatial distribution patterns associated with torrential rainfall days that linked to the topography of Peninsular Malaysia.This is done by applying cluster analysis on the most relevant principal directions extracted from a principal components analysis of the between day correlation. However, the characteristic of rainfall data in Peninsular Malaysia involve skewed observations which only take positive values and are skewed towards higher values. Thus, applying PCA based Pearson correlation on rainfall data set could affect cluster partitions and generate extremely unbalanced clusters. Tukey's biweight correlation is introduced to overcome the problem where the weight function down weights data values that is far from the center of the data. The findings indicate that ten rainfall patterns obtained are quite definite and clearly display the dominant role extended by the complex topography and exchange monsoons of the peninsular.

References

[1] Behera, H.S., Ghosh, A., and Mishra, S.K. (2012). A New Improved Hybridized K Means Clustering Algorithm with Improved PCA Optimized with PSO for High Dimensional Data Set. International Journal of Soft Computing and Engineering (IJSCE), 2,2231-2307.

[2] Bunkers, M.J., Miller, J.R., and DeGaetano A.T.(1996), Definition of climate regions in the Northern Plains using an objective cluster modification technique, Journal of Climate, 9,130-146.

[3] Chapman, T.G.(1998). Stochastic modelling of daily rainfall: The impact of adjoining wet days on the distribution of rainfall amounts. Environmental Modelling and Software, 13,317-324.

[4] Choulakian, V. (2001). Robust q-mode principal component analysis in L1. Computational Statistics & Data Analysis, 37,135-150.

[5] Compagnucci, R.H., Araneo,D., and Canziani, P.O. (2001). Principal sequence pattern analysis : A new approach to classifying the evolution of atmospheric systems. International Journal of Climatology, 21,197-217.

[6] Fragoso, M., and Gomes, P.T. (2008). Classification of Daily Abundant Rainfall Patterns and Associated Large-Scale Atmospheric Circulation Types in Southern Portugal. International Journal of Climatology,

[7] Halkidi, M., Batistakis, Y., and Vazirgiannis, M.(2001). On clustering validation techniques. Journal of Intelligent Systems, 17,107-145.

[8] Hardin, J., Mitani. A., Hicks. L., and Vankoten. B.(2007). A robust measure of correlation between two genes on a microarray. BMC Bioinformatics, 8,220.

[9] Jamaludin, S., and Jemain A.A. (2007). Fitting the statistical distributions to the daily rainfall amount in Peninsular Malaysia. Jurnal Teknologi, 46:33-48

[10] Jamaludin, S., MohdDeni, S., Wanzin, W.Z., and Jemain, A.A. (2010). Trends in PeninsulatMalaysia Rainfall Data during the Southwest Monsoon and Northeast Monsoon Seasons. Sains Malaysian, 39(4),533-542.

[11] Jolliffe, I.T.(2002). Principal Component Analysis. (2nd ed.). New York,Inc. : Springer-Verlag.

[12] Kafadar, K.(1983). The efficiency of the biweight as a robust estimator of location. Journal of Research of the National Bureau of Standard, 88,105-116.

[13] Mimmack, G.M., Mason S.J., and Galpin, J.S.(2000). Choice of distance matrices in cluster analysis:Defining regions. Journal of Climate, 14,2790-2797

[14] Penarrocha, D., Estrela, M.J., and Millan, M.( 2002). Classification of Daily Rainfall Patterns in a Mediterranean Area with Extreme Intensity Levels: The Valencia Region. Internation Journal of Climatology, 22,677-695

[15] Romero, R., Ramis, C., and Guijarro, J.A. (1999). Daily Rainfall Patterns in the Spanish Mediterranean Area: An Objective Classification. International Journal of Climatology, 19,95-112

[16] Sumner, G., Guijarro. J.A., Ramis, C. (1995). The impact of surface circulations on the daily rainfall over Mallorca. International Journal of Climatology, 15, 673–696

[17] Wickramagamage, P. (2010). Seasonality and spatial pattern of rainfall of Sri Lanka: Exploratory factor analysis. International Journal of Climatology, 30,1235-1245

[18] Wong, C.L., Venneker, R., Uhlenbrook, S., Jamil, A.B.M., and Zhou, Y. (2009). Variability of rainfall in Peninsular Malaysia. Hydrology and earth system sciences discussions, 6, 5471-5503

[19] Shaharudin, S.M., Ahmad, N., Yusof, F. (2013). The Comparison of T-Mode and Pearson Correlation Matrices in Classification of Daily Rainfall Patterns in Peninsular Malaysia. Matematika. 29 (1c), 187-194.

[20] Shaharudin, S.M., Ahmad, N., Yusof, F. (2013). Improved Cluster Partition in Principal Component Analysis Guided Clustering. International Journal of Computer Applications. 75(11), 22-25.

[21] Shaharudin, S.M., Ahmad, N., Yusof, F., Mohamed, N.S. (2018). Identification of Rainfall Patterns on Hydrological Simulation using Robust Principal Component Analysis. Indonesian Journal of Electrical Engineering and Computer Science. 11(3), 1188-1193.

[22] Samsudin, M.S., Azid, A., Khalit, S.I., Shaharudin, S.M., Lananan, F., Juahir, H. (2018). Pollution Sources Identification of Water Quality Using Chemometrics: a Case Study in Klang River Basin, Malaysia. International Journal of Engineering & Technology. 7(4.43), 83-89.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page