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Myanmar is an agricultural country and its economy is largely based upon crop productivity. The occurrence of extreme precipitation variability may lead to significantly reduce crop yields and extensive crop losses. Thus, rainfall prediction becomes an important issue in Myanmar. Regression has since long been a major data analytic tool in many scientific such as behavioral sciences, social sciences, biological sciences, medical sciences, psychometrics and econometrics for predicting. Multi variables polynomial regression (MPR) is one of the statistical regression method used to describe complex nonlinear input output relationships. In this paper, MPR is applied to implement the precipitation forecast model over Myanmar. Myanmar receives its annual rainfall during the summer monsoon season which starts in June and end in September. The model output result is station wide monthly and annual rainfall amount during summer monsoon season. The proposed model results are compared with the result produced by multiple linear regression model (MLR). From the experimental results, it is observed that using MPR method achieves closer agreement between actual and estimated rainfall than using MLR

Road traffic congestion is headache major problem in urban area of both developing and developed countries. In order to reduce this problem, traffic congestion states of road networks are estimated so that congested road can be avoided. In this paper, we estimate the real time traffic congestion states of user's desired source and destination and present the estimated results in Google Map. We use Hidden Markov model (HMM) for estimating the traffic condition states of these road network using both historical and real time data. To get these traffic data we use GPS trajectories data collected from mobile phones on vehicles. We evaluate our estimating system using dataset generated by collect data from phone-equipped vehicles over a period of 4 months in Yangon.