Empirical Statistical Modeling of Rainfall Prediction over Myanmar

Authors: Wint Thida Zaw, Thinn Thu Naing

Abstract:

One of the essential sectors of Myanmar economy is agriculture which is sensitive to climate variation. The most important climatic element which impacts on agriculture sector is rainfall. Thus rainfall prediction becomes an important issue in agriculture country. Multi variables polynomial regression (MPR) provides an effective way to describe complex nonlinear input output relationships so that an outcome variable can be predicted from the other or others. In this paper, the modeling of monthly rainfall prediction over Myanmar is described in detail by applying the polynomial regression equation. The proposed model results are compared to the results produced by multiple linear regression model (MLR). Experiments indicate that the prediction model based on MPR has higher accuracy than using MLR.

Keywords: Polynomial Regression, Rainfall Forecasting, Statistical forecasting.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084254

References:

[1] N. Sen, " New forecast models for Indian south-west Monsoon season Rainfall", in Current Science, vol. 84, No. 10, May 2003, pp.1290- 1291.
[2] S. Nkrintra, et al., "Seasonal Forecasting of Thailand Summer Monsoon Rainfall", in International Journal of Climatology, Vol. 25, Issue 5, American Meteorological Society, 2005, pp. 649-664.
[3] T. Sohn, J. H. Lee, S. H. Lee, C. S. Ryu, "Statistical Prediction of Heavy Rain in South Korea", in Advances in Atmospheric Sciences, Vol. 22, No. 5, 2005, pp.703-710.
[4] M. T. Mebrhatu, M. Tsubo, S. Walker, "A Statistical Model for Seasonal Rainfall Forecasting over the Highlands of Eritrea", in International Crop Science Organization.
[5] H. Hasani, M. Zokaei, A. Amidi,"A New Approach to Polynomial Regression And Its Application to Physical Growth of Human Height".
[6] D.A. Vaccari, J. Levri, "Multivariable Empirical Modeling of ALS Systems Using Polynomials," in Life Support and Biosphere Science, 1999, vol. 6 pp. 265-271.
[7] http://www.cru.uea.ac.uk/.
[8] http://www.cpc.noaa.gov/.
[9] http://www.jamstec.go.jp/