Statistical downscaling for rainfall and temperature prediction in Thailand

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dc.contributor.authorAksornsingchai, Pawanrat
dc.contributor.authorSrinilta, Chutimet
dc.date.accessioned2026-08-06T10:02:31Z
dc.date.available2026-08-06T10:02:31Z
dc.date.issued2011-07-26
dc.description.abstractThis paper studies three statistical downscaling methods to predict temperature and rainfall at 45 weather stations in Thailand. Methods under consideration are multiple linear regressions (MLR), support vector machine with polynomial kernel (SVM-POL), and support vector machine with Radial Basis Function kernel (SVM-RBF). Large-scale data are from Geophysical Fluid Dynamics Laboratory (GFDL). Five predictor variables are chosen: (1) temperature, (2) pressure, (3) precipitation, (4) evaporator, and (5) net short wave. Accuracy is assessed by 10-fold cross-validation in terms of root-mean-squared error (RMSE) and correlation coefficient (R). SVM-RBF is the most accurate model. Prediction accuracy of monthly average rainfall and temperature is satisfying in most part of the country. Lastly, downscaling models can project long term trends of monthly average rainfall and temperature.
dc.identifier.citationImecs 2011 International Multiconference of Engineers and Computer Scientists 2011, 1, 356-361, 2011
dc.identifier.other2-s2.0-79960616010
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/3686
dc.sourceImecs 2011 International Multiconference of Engineers and Computer Scientists 2011
dc.subjectMultiple linear regressions
dc.subjectRainfall
dc.subjectStatistical downscaling
dc.subjectSupport vector machine
dc.subjectTemperature
dc.titleStatistical downscaling for rainfall and temperature prediction in Thailand
dc.typeConference Paper

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