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. I. INTRODUCTION AINFALL information is important for food production plan, water resource management and all activity plans in the nature. The occurrence of prolonged dry period or heavy rain at the critical stages of the crop growth and development may lead to significant reduce crop yield. Myanmar is an agricultural country and its economy is largely based upon crop productivity. Thus rainfall prediction becomes a significant factor in agricultural countries like Myanmar. A wide range of rainfall forecast methods are employed in weather forecasting at regional and national levels. Fundamentally, there are two approaches to predict rainfall. They are empirical and dynamical methods. The empirical approach is based on analysis of historical data of the weather and its relationship to a variety of atmospheric and oceanic variables over different parts of the world. The most widely use empirical approaches used for climate prediction are regression, artificial neural network, stochastic, fuzzy logic and group method of data handling. In dynamical approach, predictions are generated by physical models based on systems of equations that predict the evolution of the global climate system in response to initial atmospheric conditions. The dynamical approaches are implemented using numerical weather forecasting method. Wint Thida Zaw is with the Umiversity of Computer Studies, Yangon, Myanmar (corresponding author to provide phone: 95-1-661713; fax: 95-1- 665686; e-mail: wintthida@gmail.com). Thinn Thu Naing is with the Umiversity of Computer Studies, Yangon, Myanmar (corresponding author to provide phone: 95-1-661713; fax: 95-1- 665686; e-mail:ucsy21@most.gov.mm). In this paper, rainfall prediction model is implemented with the use of empirical statistical technique, MPR. We use 37 years (1970-2006) datasets of the global climate data such as sea surface temperature (SST), India Ocean Dipole (IOD), Southern Oscillation Index (SOI), Oceanic Nino Index (ONI) and premonsoon month rainfall amount over 17 Myanmar weather stations as local weather data. The model forecasts monthly rainfall amount in summer monsoon season (in mm). The resulted rainfall amounts are intended to help farmers in making decision concerning with their crop. It is possible to predict monthly rainfall amount with one month ahead with acceptably accuracy. The experimental results show that there is a close agreement between the predicted and actual rainfall amount. II. RELATED WORK Accurate and timely weather forecasting is a major challenge for the scientific community. Rainfall prediction modeling involves a combination of computer models, observation and knowledge of trends and patterns. Using these methods, reasonably accurate forecasts can be made up. Several recent research studies have developed rainfall prediction using different weather and climate forecasting methods. Regression is a statistical empirical technique and is widely used in business, the social and behavioral sciences, the biological sciences, climate prediction, and many other areas. N. Sen [1] has presented long-range summer monsoon rainfall forecast model based on power regression technique with the use of Ei Nino, Eurasian snow cover, north west Europe temperature, Europe pressure gradient, 50 hPa Wind pattern, Arabian sea SST, east Asia pressure and south Indian ocean temperature in previous year. The experimental results showed that the model error was 4%. S. Nkrintra, et al. [2] described the development of a statistical forecasting method for SMR over Thailand using multiple linear regression and local polynomial-based nonparametric approaches. SST, sea level pressure (SLP), wind speed, EiNino Southern Oscillation Index (ENSO), IOD were chosen as predictors. The experiments indicated that the correlation between observed and forecast rainfall was 0.6. T. Sohn, et al. [3] has developed a prediction model for the occurrence of heavy rain in South Korea using multiple linear and logistics regression, decision tree and artificial neural network. They used 45 synoptic factors generated by the numerical model as potential predictors. Empirical Statistical Modeling of Rainfall Prediction over Myanmar Wint Thida Zaw and Thinn Thu Naing R World Academy of Science, Engineering and Technology 46 2008 565