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Author Aksornsingchai, Pawanrat ♦ Srinilta, Chutimet
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Temperature Prediction ♦ Support Vector Machine ♦ Statistical Downscaling ♦ Multiple Linear Regression ♦ Monthly Average Rainfall ♦ Prediction Accuracy ♦ Root-mean-squared Error ♦ Predictor Variable ♦ Polynomial Kernel ♦ Index Term Statistical Downscaling ♦ 10-fold Cross-validation ♦ Large-scale Data ♦ Long Term Trend ♦ Accurate Model ♦ Net Short Wave ♦ Correlation Coefficient ♦ Weather Station ♦ Paper Study ♦ Geophysical Fluid Dynamic Laboratory ♦ Radial Basis Function Kernel
Abstract Abstract—This 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. Index Terms — statistical downscaling, temperature, rainfall, multiple linear regressions, support vector machine.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study