CLUSTERING OF RAINFALL DISTRIBUTION PATTERNS IN PENINSULAR MALAYSIA USING TIME SERIES CLUSTERING METHOD

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Published: Sep 30, 2019
DOI: https://doi.org/10.22452/mjs.sp2019no2.8
Keywords:
time series clustering dissimilarity measures rainfall patterns Peninsular Malaysia

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Noratiqah Mohd Ariff
Universiti Kebangsaan Malaysia
Mohd Aftar Abu Bakar
Universiti Kebangsaan Malaysia
Sharifah Faridah Syed Mahbar
Kementerian Tenaga, Sains, Teknologi, Alam Sekitar & Perubahan Iklim
Mohd Shahrul Mohd Nadzir
Universiti Kebangsaan Malaysia

Abstract

Time series clustering technique was used in this study to categorize the locations in Peninsular Malaysia according to the similarity of rainfall distribution patterns. Daily rainfall time series data from 12 meteorological observation stations across Peninsular Malaysia have been considered for this study. Four dissimilarity measure methods were examined and compared in terms of accuracy and suitability, namely Euclidean distance (ED), complexity-invariant distance (CID), correlation-based distance (COR) and integrated periodogram-based distance (IP). The average silhouette width (ASW) was used to determine the optimal group number for the rainfall time series data. Using Ward’s hierarchical clustering method, this study found that the rainfall time series in Peninsular Malaysia can be divided into four regions of homogeneous climate zones. Based on the results, the IP was the most suitable dissimilarity measures for clustering rainfall time series data in Peninsular Malaysia, except during the Southwest Monsoon where the COR performed better.

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How to Cite
Mohd Ariff, N., Abu Bakar, M. A., Syed Mahbar, S. F., & Mohd Nadzir, M. S. (2019). CLUSTERING OF RAINFALL DISTRIBUTION PATTERNS IN PENINSULAR MALAYSIA USING TIME SERIES CLUSTERING METHOD. Malaysian Journal of Science, 38(Sp2), 84–99. https://doi.org/10.22452/mjs.sp2019no2.8
Issue
Vol. 38 No. Sp2 (2019): Special Issue International Seminar on Mathematics in Industry & International Conference on Theoretical and Applied Statistics 2018 (ISMI-ICTAS18)
Section
ISMI-ICTAS18 (Published)

Licensee MJS, Universiti Malaya, Malaysia. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

References

Aghabozorgi, S., Shirkhorshidi, A.S. & Wah, T.Y. (2015). Time-series clustering–A decade review. Information Systems, 53: 16-38.

Ahmad N.H., Othman I.R. & Deni S.M. (2013). Hierarchical cluster approach for regionalization of Peninsular Malaysia based on the precipitation amount. Journal of Physics: Conference Series, 423(1): 12-18.

Ariff N.M., Bakar M.A.A. & Rahmad M.I. (2018). Comparative study of document clustering algorithms. International Journal of Engineering and Technology (UAE), 7(4): 246-251.

Ariff N.M., Jemain A.A. & Bakar M.A.A. (2016). Regionalization of IDF curves with L-moments for storm events. International Journal of Mathematical and Computational Sciences, 10: 217-223.

Arifin A.Z. & Asano A. (2006). Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern Recognition Letters, 27(13): 1515-1521.

Batista G.E., Keogh E.J., Tataw O.M. & De Souza V.M. (2014). CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28(3): 634-669.

Crétat J., Richard Y., Pohl B., Rouault M., Reason C. & Fauchereau N. (2012). Recurrent daily rainfall patterns over South Africa and associated dynamics during the core of the austral summer. International Journal of Climatology, 32(2): 261-273.

De Lucas D.C. (2010). Classification Techniques for Time Series and Functional Data. Universidad Carlos III de Madrid. Doctoral dissertation.

DeGaetano A.T. (2001). Spatial grouping of United States climate stations using a hybrid clustering approach. International Journal of Climatology, 21(7): 791-807.

Han J., Pei J. & Kamber M. (2012). Data Mining: Concepts and Techniques 3rd Edition. Waltham, M.A.: Morgan Kaufmann Publishers.

Kavitha V. & Punithavalli M. (2010). Clustering time series data stream–a literature survey. International Journal of Computer Science and Information Security, 8(1):289-294.

Lin J. & Li Y. (2009). Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation. In Proceedings of the 21st International Conference on Scientific and Statistical Database Management, 461-477.

Michinaka T., Tachibana S. & Turner J.A. (2011). Estimating price and income elasticities of demand for forest products: cluster analysis used as a tool in grouping. Forest Policy and Economics, 13(6): 435-445.

Munoz-Diaz D. & Rodrigo F.S. (2004). Spatio-temporal patterns of seasonal rainfall in Spain (1912-2000) using cluster and principal component analysis: comparison. Annales Geophysicae, 22(5): 1435-1448.

Prasanna K.A.V.L. (2012). Performance evaluation of multiviewpoint-based similarity measure for data clustering. Journal of Global Research in Computer Science, 3(11): 21-26.

Ramos M.C. (2001). Divisive and hierarchical clustering techniques to analyse variability of rainfall distribution patterns in a Mediterranean region. Atmospheric Research, 57(2):123-138.

Maharaj E.A., D’Urso P. & Galagedera D.U. (2010). Wavelet-based fuzzy clustering of time series. Journal of Classification, 27(2): 231-275.

Rani, S. & Sikka, G. (2012). Recent techniques of clustering of time series data: a survey. International Journal of Computer Applications, 52(15): 1-9.

Soltani S. & Modarres R. (2006). Classification of spatio-temporal pattern of rainfall in Iran using a hierarchical and divisive cluster analysis. Journal of Spatial Hydrology, 6(2): 1-12.

Tennant W.J. & Hewitson B.C. (2002). Intra-seasonal rainfall characteristics and their importance to the seasonal prediction problem. International Journal of Climatology: A Journal of the Royal Meteorological Society, 22(9): 1033-1048.