Comparison Study on Modelling and Prediction of Weather Parameters Combining Exponential Smoothing and Artificial Neural Network Models in Different Zones of Kerala

Abstract

Prediction of weather parameters with maximum precision is the main objective faced by different climatologists all over the world. The researchers devised a number of ways for performing weather parameter predictions. For modelling and prediction of weather parameters, exponential smoothing (ETS), artificial neural networks (ANN) and ETS-ANN hybrid models were employed for the northern, central and southern zones of Kerala. The weather parameters used in the study are relative humidity and wind speed. The monthly data were collected for the northern and central zones, for a period of 39 years (1982-2020), whereas for the southern zone it was for a period of 36 years (1985-2020). The result suggested that the best fitted model for relative humidity was the ANN for the central and southern zones whereas for northern zone it was the ETS model. The accuracy of the model was calculated using MSE, RMSE, MAE, MAPE and R2 values. The ETS performed best in all three zones of Kerala when it came to wind speed. The study also advocated that traditional model ETS and ANN is performing better compared to the newly developed hybrid model in prediction of weather parameters. The best model chosen for weather parameters of three different zones are used to forecast for the next five years.