A Comparative Forecasting Model of Monthly Rainfall in the Northeast of Thailand
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Abstract
The purpose of this study was to compare 5 forecasting models, i.e. Ratio-to-Trend Method, Holt-Winters Exponential Smoothing Method, Regression Dummy Variables Method, Theta Method, and Combined Forecasting Method. These methods were used to forecast the maximum monthly rainfall (MMR) and accumulated monthly rainfall (AMR) in 3 northeastern provinces of Thailand, including Sakon Nakhon, Nakhon Phanom and Mukdahan. Related data collected by Meteorological Department of Thailand during 1984 through 2017 were investigated for the accuracy of the forecasting models. The Mean Square Error (MSE) was used to determine the model accuracy while the smallest MSE values indicate the optimal forecasting model. The R programming was used for data analysis. Rainfall time series and seasonal variability were detected. As results, the optimal model was found to be Combined Forecasting Method; followed by other 4 tops models: the Ratio-to-Trend Method, Theta Method, Holt-Winters Exponential Smoothing Method and Regression Dummy Method respectively.
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References
[2] R. Nansaior, “Forecasting of rainfall for crop modelling plan in Northeast Thailand,” M.S. thesis, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, 1999 (in Thai).
[3] J. Kittinapakul, “Comparison of seasonal dataanalysis by Box-Jenkin and Holt-Winter exponential smoothing methods,” M.S. thesis, Graduate School, Chiang Mai University, 2000 (in Thai).
[4] W. Panichkitkosolkul,“Monthly rainfall amount forecasting of meteorological station and agrometeorological stations in Northeastern Thailand,”Thai Journal of Science and Technology, vol. 17, no. 2, pp. 1–12, 2009.
[5] P. Ruangchaisiwawet, “Forecasting monthly rainfall of Lampang province by statistical forecasting techniques,” M.S. thesis, Department of Statistics, Faculty of Science, Kasetsart University, 2011 (in Thai).
[6] W. Keerativibool, “Forecasting the rainfall in Muang, Nan province,” KMUTT Research and Development Journal, vol. 38, no.3, pp. 211–223, 1999.
[7] M. Manmin, Time Series and Forecasting. Bangkok: Four Printing, 2006 (inThai).
[8] S. Damsri and K. Maneewong, “A comparative study of forecasting models for the number of Malaria patients in Phanom district,” Journal of Thai Interdisciplinary, vol. 12, no. 3, pp. 1–6, 2017.