Artificial Neural Network based Weather Prediction using Back Propagation Technique

Weather forecasting is a natural phenomenon which has some chaotic changes happening with the passage of time. It has become an essential topic of research due to some abrupt scenarios of weather. As the data of forecast is nonlinear and follows some irregular trends and patterns, there are many traditional techniques (the literature like nonlinear statistics) to work on the efficiency of models to make prediction better than previous models. However, Artificial Neural Network (ANN) has so far evolved out to be as a better way to improve the accuracy and reliability. The ANN is one of the most fastest growing technique of machine learning considered as non-linear predictive models to perform classification and prediction weather forecasts maximum temperature for the whole days (365) of the year. Therefore, a multi-layered neural network is designed and trained with the existing dataset and obtained a relationship between the existing non-linear parameters of weather. Eleven weather features were used to perform classification of weather into four types. Furthermore, twenty training examples from 1997-2015 were used to predict eleven weather features. The results revealed that by increasing the number of hidden layers, the trained neural network can classify and predict the weather variables with less error.