Multivariable Panel Data Cluster Analysis of Meteorological Stations in Thailand for ENSO Phenomenon

Porntip Dechpichai; Nuttawadee Jinapang; Pariyakorn Yamphli; Sakulrat Polamnuay; Sittisak Injan; Usa Humphries

doi:10.3390/mca27030037

Mathematical and Computational Applications (Apr 2022)

Multivariable Panel Data Cluster Analysis of Meteorological Stations in Thailand for ENSO Phenomenon

Porntip Dechpichai,
Nuttawadee Jinapang,
Pariyakorn Yamphli,
Sakulrat Polamnuay,
Sittisak Injan,
Usa Humphries

Affiliations

Porntip Dechpichai: Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mot, Thung Khru, Bangkok 10140, Thailand
Nuttawadee Jinapang: Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mot, Thung Khru, Bangkok 10140, Thailand
Pariyakorn Yamphli: Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mot, Thung Khru, Bangkok 10140, Thailand
Sakulrat Polamnuay: Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mot, Thung Khru, Bangkok 10140, Thailand
Sittisak Injan: Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mot, Thung Khru, Bangkok 10140, Thailand
Usa Humphries: Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mot, Thung Khru, Bangkok 10140, Thailand

DOI: https://doi.org/10.3390/mca27030037
Journal volume & issue: Vol. 27, no. 3
p. 37

Abstract

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The purpose of this research is to study the spatial and temporal groupings of 124 meteorological stations in Thailand under ENSO. The multivariate climate variables are rainfall, relative humidity, temperature, max temperature, min temperature, solar downwelling, and horizontal wind from the conformal cubic atmospheric model (CCAM) in years of El Niño (1987, 2004, and 2015) and La Niña (1999, 2000, and 2011). Euclidean distance timed and spaced with average linkage for clustering and silhouette width for cluster validation were employed. Five spatial clusters (SCs) and three temporal clusters (TCs) in each SC with different average precipitation were compared by El Niño and La Niña. The pattern of SCs and TCs was similar for both events except in the case when severe El Niño occurred. This method could be applied using variables forecasted in the future to be used for planning and managing crop cultivation with the climate change in each area.

Published in Mathematical and Computational Applications

ISSN: 1300-686X (Print); 2297-8747 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods; Science: Mathematics: Instruments and machines: Electronic computers. Computer science
Website: http://www.mdpi.com/journal/mca

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