Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey
Emre Yakut, Seval Süzülmüş
Abstract
The accurate modelling and prediction of air temperature values is an exceptionally important meteorological variable that affects in many areas. The present study is aimed at developing models for the prediction of monthly mean air temperature values in Turkey using ANN, ANFIS and SVMr methods. In developing the models, the monthly data derived from eight stations of the TSMS for the 1963–2015 period were used, including latitude, longitude, elevation, month, and minimum, maximum and mean air temperatures. The performances of the ANN, ANFIS and SVMr models were compared using R2, MSE, MAPE and RRMSE. In order to verify the differences between the predicted temperature values provided by the ANN, ANFIS and SVMr models and the observed temperature values derived from the stations, a t-test analysis was conducted, and the best ANN, ANFIS and SVMr models were determined according to the statistical performance values. These models were then used to make air temperature predictions for the cities. Manova was carried out to determine the effects of the differences temperature predictions and RRMSE values of the models. Generally, the statistical performance values of the ANFIS models were found to be slightly better than those of the ANN and SVMr models.