CLUSTERING OF RAINFALL DISTRIBUTION PATTERNS IN PENINSULAR MALAYSIA USING TIME SERIES CLUSTERING METHOD
Article Sidebar
Published:
Sep 30, 2019
Keywords:
time series clustering dissimilarity measures rainfall patterns Peninsular Malaysia
Main Article Content
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.
Downloads
Download data is not yet available.
Article Details
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
Section
ISMI-ICTAS18 (Published)
Transfer of Copyrights
- In the event of publication of the manuscript entitled [INSERT MANUSCRIPT TITLE AND REF NO.] in the Malaysian Journal of Science, I hereby transfer copyrights of the manuscript title, abstract and contents to the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) for the full legal term of copyright and any renewals thereof throughout the world in any format, and any media for communication.
Conditions of Publication
- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures, maps, photographs and other visual materials of whom the copyrights is owned by a third party.
- This manuscript contains no statements that are contradictory to the relevant local and international laws or that infringes on the rights of others.
- I agree to indemnify the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) in the event of any claims that arise in regards to the above conditions and assume full liability on the published manuscript.
Reviewer’s Responsibilities
- Reviewers must treat the manuscripts received for reviewing process as confidential. It must not be shown or discussed with others without the authorization from the editor of MJS.
- Reviewers assigned must not have conflicts of interest with respect to the original work, the authors of the article or the research funding.
- Reviewers should judge or evaluate the manuscripts objective as possible. The feedback from the reviewers should be express clearly with supporting arguments.
- If the assigned reviewer considers themselves not able to complete the review of the manuscript, they must communicate with the editor, so that the manuscript could be sent to another suitable reviewer.
Copyright: Rights of the Author(s)
- Effective 2007, it will become the policy of the Malaysian Journal of Science (published by the Faculty of Science, University of Malaya) to obtain copyrights of all manuscripts published. This is to facilitate:
- Protection against copyright infringement of the manuscript through copyright breaches or piracy.
- Timely handling of reproduction requests from authorized third parties that are addressed directly to the Faculty of Science, University of Malaya.
- As the author, you may publish the fore-mentioned manuscript, whole or any part thereof, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given. You may produce copies of your manuscript, whole or any part thereof, for teaching purposes or to be provided, on individual basis, to fellow researchers.
- You may include the fore-mentioned manuscript, whole or any part thereof, electronically on a secure network at your affiliated institution, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- You may include the fore-mentioned manuscript, whole or any part thereof, on the World Wide Web, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- In the event that your manuscript, whole or any part thereof, has been requested to be reproduced, for any purpose or in any form approved by the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers), you will be informed. It is requested that any changes to your contact details (especially e-mail addresses) are made known.
Copyright: Role and responsibility of the Author(s)
- In the event of the manuscript to be published in the Malaysian Journal of Science contains materials copyrighted to others prior, it is the responsibility of current author(s) to obtain written permission from the copyright owner or owners.
- This written permission should be submitted with the proof-copy of the manuscript to be published in the Malaysian Journal of Science
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.
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.