Comparison of Hybrid Model (ANFIS-PSO) and Tork Experimental Model in Reference Estimation of Evapotranspiration (Case study: Poldokhtar-Lorestan)

Document Type : Research Article

Authors

1 Faculty of Agriculture and Natural Resources, University of Lorestan

2 Faculty of New Sciences and Technologies Renewable Energies and Environment.

3 Watershed Management Engineering, Faculty of Agriculture and Natural Resources, University of Lorestan

Abstract

Evapotranspiration the catchment area is one of the main components of the water cycle and estimates the need for irrigation. The purpose of this study is to estimate the daily evapotranspiration and forwarding (ETo) using a neuro-fuzzy comparative inference system (ANFIS) and compare it with the experimental model of Tork. The input data of the turquoise model, including sunshine, air temperature, relative humidity and wind speed, were obtained from the Meteorological Station of Poldokhtar from the Lorestan Meteorological Station, and evapotranspiration was obtained using this method. Input data (ANFIS) including average temperature, mean humidity and latent evaporation rate were calculated using the corresponding and were given to the data model. The results of each method were compared separately with evaporation calculated at the station location using a vapor pan. In this study, the performance of the ANFIS based on the optimization algorithm (PSO) was satisfactory and the results were satisfactory. The efficiency of the compared methods was obtained using root mean square error (RMSE), mean square error MSE And the R2 determination coefficient was evaluated. The ANFIS-PSO method, with only three parameters, is the average of average humidity and the latent evapotranspiration rate, which can predict daily reference evapotranspiration and is more accurate than the empirical model of tork. So that the value of R2, RMSE, MSE for ANFIS model is 0.90, 2.65, 13.7 and for the experimental model of Tork is 0.63, 6.26, 39.24, respectively.

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Volume 6, Issue 3
September 2019
Pages 685-694
  • Receive Date: 22 December 2018
  • Revise Date: 21 June 2019
  • Accept Date: 21 June 2019
  • First Publish Date: 23 September 2019