Prediction of daily evaporation using hybrid support vector regression-firefly optimization algorithm and multilayer perceptron

Prediction of daily evaporation is a valuable and determinant tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. Therefore, in this study, the ability of artificial intelligence models of multi-layer perceptron (MLP), support vector regression (SVR), and the hybrid model of support vector regression-firefly optimization algorithm (SVR-FFA), to predict daily evaporation at Takab Station during the period 2002-2020 based on four statistical criteria have been assessed In all three models, the best scenario was the model whose input included the parameters of average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunny hours. Among the input parameters, the sunny hours was one of the effective components on the evaporation prediction, which reduced the errors in all models. The results showed that the sixth scenario of the MLP model provided the best performance with the least error (2.18) compared to other models. It was also concluded that the sixth scenario of the SVR-FFA model had a lower error (2.20) than the other models. Among the SVR model scenarios, the sixth scenario showed the lowest error (2.27) compared to other SVR combinations. The results of this study showed that the sixth scenario of the MLP model had the best performance and the hybrid firefly algorithm improved the performance of support vector regression in estimating daily evaporation.