An Enhanced Support Vector Regression Model for Weather Forecasting

Abstract

Abstract: An attempt is made in this paper to develop an Enhanced Support Vector Regression (ESVR ) model with more un-interpretable kernel functions in the domain of forecasting the weather conditions. Every predicate model takes input data set parameters, processing with in specified levels of classification into variable sets and countered with a variable set reduction to reach the decision of prediction in a more confirm levels. This paper also provides a critical modeling design using the non linear regression namely support vector machines which is very non interpretable neural network designed as a two stage activity in prediction with more than three kernel functions for improving the performance of the SVMs experts in predicting the future weather happenings. Recording the input data set parameters of weather like temperature, water vapor, atmospheric pressure, dew point, wind speed, wind direction, rainfall, etc. compared with a MLP (Multi Layer preceptron) classification, SVM got much more esteemed performance in forecasting any one parameter with respect to others in a two stage procedure initial with self organizing maps and with best practice of more kernel functions investigation in weather forecasting.Key Words: SVM, SOM, Kernel Functions, Classification and prediction, MLP.