Evaluation of the support vector machine, random forest and geo-statistical methodologies for predicting long-term air temperature

This study investigates the ability of support vector machine (SVM), random forest (RF) and geo-statistical (GS) methods in simulating long-term monthly air temperatures. Data of 30 locations in Iran were utilized and data scanning procedure was employed in the applications. Geographical coordinates, e.g. latitude, longitude and altitude as well as the periodicity component (month of the year), were utilized as inputs to the applied models. SVM and RF models obtained using k-fold testing were also compared with artificial neural networks (NNs) and neuro fuzzy (NF) methods employed in a previous study. Comparisons demonstrated the superiority of SVM and RF models over the NN and NF models in the present study. In all stations, the RF model with average scatter index of 0.111, average mean absolute error of 1.456 and average variance account for of 0.968 provided better estimates than the SVM model with corresponding values of 0.142, 1.855 and 0.945, respectively. Nonetheless, the obtained results revealed that the geo-statistics-based kriging method is able to spatially simulate air temperature.