In this paper, the accuracy of a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined. For this aim, a novel method named as SVM-FFA is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration ( n), maximum temperature (T max ) and minimum temperature (T min ) as inputs. The predictions

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... ccuracy of the proposed SVM-FFA model is validated compared to those of Artificial Neural Networks (ANN) and Genetic Programming (GP) models. The root mean square (RMSE), coefficient of determination (R 2 ), correlation coefficient (r) and mean absolute percentage error (MAPE) are used as reliable indicators to assess the models' performance. The attained results show that the developed SVM-FFA model provides more precise predictions compared to ANN and GP models, with RMSE of 0.6988, R 2 of 0.8024, r of 0.8956 and MAPE of 6.1768 in training phase while, RMSE value of 1.8661, R 2 value of 0.7280, r value of 0.8532 and MAPE value of 11.5192 are obtained in the testing phase. The results specify that the developed SVM-FFA model can be adjudged as an efficient machine learning technique for accurate prediction of horizontal global solar radiation.