A novel approach for DCNN in Deep Learning Based Smart Weather Forecasting

Abstract

Many economic business activities works under weather forecasting which mainly concentrates on the reliability and accuracy of the weather forecast. Here we introduce novel methods which are a data driven predictive model known to be deep convolutional neural networks (DCNN) architecture.

DCNN mainly works to predict the wind speed and temperature of the weather data. Different versions of DCNN are introduced in our deep learning framework. The evaluation result proves that our method provides better accuracy when compared with the earlier methods.