Volume 25, Issue 2 (Summer 2021)                   JWSS 2021, 25(2): 237-253 | Back to browse issues page


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Sabzevari Y, Saeidinia M. Evaluation of Experimental Models and Artificial Intelligence in Estimation of Reference Evapotranspiration (Case Study: Boroujerd Station). JWSS. 2021; 25 (2) :237-253
URL: http://jstnar.iut.ac.ir/article-1-4013-en.html
1. Department of Water Engineering, Faculty of Agriculture and Natural Resources, Lorestan University, Lorestan, Iran. , saeedinia.m@lu.ac.ir
Abstract:   (254 Views)
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.‎
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Type of Study: Research | Subject: Ggeneral
Received: 2020/04/20 | Accepted: 2020/11/17 | Published: 2021/09/1

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