Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.

Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)

Global climate change is likely to influence evapotranspiration (ET); as a result, many ET calculation methods may not give accurate results under different climatic conditions. The main objective of this study is to verify the suitability of machine learning (ML) models as calculation methods for pan evaporation modeling on the macro-regional scale. The most significant PE changes in the different agroclimatic zones of the Slovak Republic were compared, and their considerable impacts were analyzed. On the basis of the agroclimatic zones, 35 meteorological stations distributed across Slovakia were classified into six macro-regions. For each of the meteorological stations, 11 variables were applied during the vegetation period in the years from 2010 to 2020 with a daily time step. The performance of eight different ML models—the neural network (NN) model, the autoneural network (AN) model, the decision tree (DT) model, the Dmine regression (DR) model, the DM neural network (DM NN) model, the gradient boosting (GB) model, the least angle regression (LARS) model, and the ensemble model (EM)—was employed to predict PE. It was found that the different models had diverse prediction accuracies in various geographical locations. In this study, the results of the values predicted by the individual models are compared.