Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey

Performance of four different machine learning–based approaches (long short-term memory (LSTM), support vector machine regression (SVMR), Gaussian process regression (GPR), and multi-gene genetic programming (MGGP) models) in estimation of long-term monthly temperatures was investigated in this study. Data of 250 measuring stations of Turkey were used in present trials. Month numbers of the year, latitude, longitude, and altitude variables were used as input data of the models. Error statistics of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R2), and Nash−Sutcliffe efficiency coefficient (NSE) were used while comparing the four models. In terms of five error statistics, models yielded similar outcomes. Therefore, Taylor and Violin diagrams were used to assess how close the model-estimated values to measured data. Taylor and Violin diagrams revealed that GPR model had better performance in estimation of maximum and average temperatures than the other three models. Also, it was determined that the measured data estimated by the Kruskal–Wallis test came from the same distribution. At the end of this study, efficiency of the methods recommended for comparisons was proven. © 2021, Saudi Society for Geosciences.