A comparative study using a data-driven method versus a hybrid approach to estimate daily reference evapotranspiration in Ahvaz

Daily reference evapotranspiration prediction is a decisive and useful tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. The use of hybrid models with the help of climatic factors is an effective method in the daily reference evapotranspiration forecasting process. Therefore, in this study, the ability of the support vector regression model (SVR) and the combined model of support vector regression with the fruit fly algorithm (SVR-FOA) in estimating daily reference evapotranspiration in Ahvaz station during the period of 2000-2022 using four statistical criteria was evaluated. The inputs used included parameters of average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours. The sensitivity analysis of the input parameters using Pearson's correlation coefficient also showed that among the input parameters, the parameters of sunshine hours and relative humidity were effective components in the prediction of evapotranspiration, thus reducing the error in all models. The obtained results showed that the sixth scenario of the SVR-FOA model provided the best performance with the lowest error (1.24 mm/day) compared to all models. Among the scenarios of the SVR model, the third scenario of the SVR model showed the lowest error (1.45 mm/day) compared to other SVR combinations. The results of this research showed that the sixth scenario of the SVR-FOA model had the best performance, and the fruit fly hybrid algorithm improved the performance of the support vector regression in estimating daily reference evapotranspiration.