An Efficient Agro-Meteorological Model for Evaluating and Forecasting Weather Conditions Using Support Vector Machine

Weather prediction is an essential area of analysis in everyday life. Climate forecasting is one of the highly relevant attributes affecting agricultural sectors and industries. Predicting climate conditions is necessary for diverse areas. Metrological department facing the greater challenge to predict the state of the environmental temperature to forecast the weather conditions based on the present, future time for expecting the rainfall. This paper majorly focuses on handling Weather data using big data statistical analysis and for effective forecasting. Support Vector Machine (SVM) predictive based modeling is used for classifying the weather dataset by using regression analysis and thereby forecasting for predicting weather conditions which is suitable for agriculture. Experiment the input dataset parameters of weather like mean temperature, mean dew point, max_sustained wind speed, mean sea level pressure, mean station pressure max_ temperature, min_ temperature, precipitation amount, max_wind gust, snow depth. The results are compared with single decision tree.