International Journal of Engineering and Technology

The most entangled system always gives pretty trouble in decision making. One such system is weather prediction with perfect set of inputs. Two sources of jangle need to consider here. One is uncertainty in the inputs and second is imperfection in mathematical model which is supposed to solve the forecast equations. As the weather forecast is a chaotic system, tiny error in the initial state can lead to the large error in the output. The uncertainty falls in two categories. Stochastic uncertainty, which is the changing behavior of the system and the epistemic uncertainty which is hard to set the initial condition of a parameter in the mathematical model due to dynamic nature. This proposed forecast model has the answer for the ongoing issue by means of incorporating sampling method to calculate the true set of inputs to be used in the forecast model. Applying Regression equation, this takes the available weather statistics and gives linear and pellucid relationship of the forecast with least error. Our model has taken several Atmospheric parameters as predictors and the weather is decomposed to several features to get accuracy to its peak. This model exhibit best accuracy, however the predictor/feature is by identifying and discarding the unwanted data and predictor which made least impact on the result without compromising the quality of the prediction. Further identified predictor may refine using neural network to get more reliable result. This study facilitates the duo to bring out notable difference in prevailing method to render the uncertainty along with addressing mathematical imperfection.