Extreme Value Modeling of Maximum Temperature in Upper Northeastern of Thailand
Abstract
The purpose of this research is to find the extreme value modeling of maximum temperature in the upper northeastern of Thailand from 9 meteorological stations using the generalized extreme value distribution. The parameter estimations are obtained from maximum likelihood estimation method under 6 different stationary and non-stationary process settings. The model selections are obtained by 3 criterions: Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and likelihood ratio test (LRT). The return levels of annual maximum temperature are calculated. Moreover, profile likelihood method is applied to calculate the confidence interval of return levels. The results of this study indicate that the 5 meteorological stations were appropriated for stationary and the 4 meteorological stations were appropriated for non-stationary when the scale parameter changed with the fitted trend. When the return levels are considered, Udon Thani meteorological station had a higher return level than other stations for each return period. Hence, this station should be more emphasized.
Keywords : generalized extreme value distribution, maximum likelihood estimation, return level,
profile likelihood method
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Busababodhin, P., & Kaewmun, A. (2015). Extreme Values Statistics. The Journal of KMUTNB, 25(2),
– 324. (in Thai)
Busababodhin, P., Siriboon, M., & Kaewmun, A. (2015). Modeling of Extreme Precipitation in Upper Northeast
of Thailand. Burapha Science Journal, 20(1), 106 – 117. (in Thai)
Charin, B. (2014). Modeling for Extreme Temperature in Central Northeast of Thailand. Master of Science
Thesis in Applied Statistics, Graduate School, Khon Kaen University. (in Thai)
Coles, S. (2004). An Introduction to Statistical Modeling of Extreme Values. (3). London: London Berlin
Heidelberg.
Ganghair, G. (2017). Health Articles Dealing with Sunstroke. Retrieved September 18, 2018, from
http:// www.thaihealth.or.th/partnership/Content/35859-รับมือลมแดด-เพลียแดด.html. (in Thai)
Khamsorn, P. (2012). Weather in Northeast. Retrieved December 1, 2018, from https://koethehero.word
press.com (in Thai)
Nakpalat, P., & Angchuan, P. (2015). Natural Disaster in Thailand. Retrieved September 18, 2018, from
https://sites.google.com/site/phaythrrmchatiniprathesthiy/hlak-kar-laea-thvsti-thi-keiywkhxng/phumi-
xakas-laea-phumiprathes-khxng-prathesthiy. (in Thai)
Office of the National Economic and Social Development Council. (2018). North East Development Plan
During the 2nd National Economic and Social Development Plan (year 2017 - 2021). Retrieved
December 1, 2018, from http://www.nesdb.go.th/ewt_dl_link.php?nid=7526. (in Thai)
R Core Team. (2019). R: A language and Environment for Statistical Computing. Vienna, Austria. from
https://www.R-project.org/
Thairath Online. (2018). ONWR Following the Drought, East Joins Various Parties Assess the Situation.
Retrieved December 1, 2018, from https://www.thairath.co.th/content/1400810. (in Thai)
The Royal Society. (2018). Statistics Dictionary (the Royal Society Edition). (2). Bangkok: Office of the Royal
Society. (in Thai)
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