علوم آب و خاک (Sep 2021)
Evaluation of Experimental Models and Artificial Intelligence in Estimation of Reference Evapotranspiration (Case Study: Boroujerd Station)
Abstract
The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with lower input data and appropriate accuracy is necessary. The purpose of this study is to investigate the capability of the experimental models, gene expression programming, stepwise regression, and Bayesian network in estimating reference evapotranspiration. In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. Based on the correlation between input and output parameters, six input patterns were determined for modeling. The results showed that the Kimberly-Penman model has the best performance among the experimental models. Gene expression programming with fourth pattern and Default Model Operators (R2 = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R2=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R2 = 0.91 and RMSE = 0.9 in the training stage. Comparison of the performance of the three models showed that the gene expression programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94. The results showed that gene expression programming had an acceptable ability to estimate reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a suitable model.
