ESTIMATION OF REFERENCE EVAPOTRANSPIRATION (ETO) USING EMPIRICAL MODELS, ARTIFICIAL NEURAL NETWORK MODELING AND THEIR COMPARISON WITH LYSIMETER DATA IN URMIA KAHRIZI STATION

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HOZHABR HASSAN; MOAZED HADI; SHOKRIKHOOCHAK SAEED

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Journal: Journal of Irrigation and Water Engineering Year:2014 | Volume:4 | Issue:15 Page(s): 13-25

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Title

ESTIMATION OF REFERENCE EVAPOTRANSPIRATION (ETO) USING EMPIRICAL MODELS, ARTIFICIAL NEURAL NETWORK MODELING AND THEIR COMPARISON WITH LYSIMETER DATA IN URMIA KAHRIZI STATION

Author(s)

HOZHABR HASSAN | MOAZED HADI | SHOKRIKHOOCHAK SAEED | Issue Writer Certificate

Keywords

EVAPOTRANSPIRATIONQ2

ARTIFICIAL NEURAL NETWORKSQ2

URMIA KAHRIZIQ2

LYSIMETERQ2

EMPIRICAL MODELSQ2

MATLABQ1

Abstract

One of methods to reduce water losses in fields is correct programming of irrigation and accurately estimate the crop water requirement form the basis of this programming that is the coefficient of Reference EVAPOTRANSPIRATION. Reference EVAPOTRANSPIRATION is a complex and multivariate phenomenon that depends on climatic factors and most accurate way to estimate it is LYSIMETER but using LYSIMETER requires a lot of time and money, hence the EVAPOTRANSPIRATION estimation is done by meteorological parameters and applying EMPIRICAL MODELS. These models have the coefficients that each coefficient is representative of regional conditions that equation is calibrated in that area. According to that the EVAPOTRANSPIRATION process is complex and nonlinear, therefore using of methods that can this complexity of modeling, it seems necessary. Therefore in this study was used of ARTIFICIAL NEURAL NETWORKS for EVAPOTRANSPIRATION modeling and in this context of MATLAB software was used. The purpose of this study was to evaluate ARTIFICIAL NEURAL NETWORKS and 11 methods known in the estimation of reference crop EVAPOTRANSPIRATION for URMIA KAHRIZI Research Station. based on daily meteorological data and 4-years data from LYSIMETER of the station, EVAPOTRANSPIRATION was calculated to above methods. The results of calculations showed that the Artificial Neural Network has better performance than all the classical methods, it has a RMSE, MAE and R2 respectively is equal to 9.65 (mm/10 day), 7.53 (mm/10 day) and 0.804. Also among the classical method, the Turc with the lowest RMSE, MAE and R2 equal to 11.69 (mm/10 day), 8.99 (mm/10 day) and 0.719 is a priority. Jensen-Haise, Penman-Monteith-Fao 56 and etc methods has been corrected in the next priorities.

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APA: Copy

HOZHABR, HASSAN, MOAZED, HADI, & SHOKRIKHOOCHAK, SAEED. (2014). ESTIMATION OF REFERENCE EVAPOTRANSPIRATION (ETO) USING EMPIRICAL MODELS, ARTIFICIAL NEURAL NETWORK MODELING AND THEIR COMPARISON WITH LYSIMETER DATA IN URMIA KAHRIZI STATION. IRANIAN OF IRRIGATION & WATER ENGINEERING, 4(15), 13-25. SID. https://sid.ir/paper/247371/en

Vancouver: Copy

HOZHABR HASSAN, MOAZED HADI, SHOKRIKHOOCHAK SAEED. ESTIMATION OF REFERENCE EVAPOTRANSPIRATION (ETO) USING EMPIRICAL MODELS, ARTIFICIAL NEURAL NETWORK MODELING AND THEIR COMPARISON WITH LYSIMETER DATA IN URMIA KAHRIZI STATION. IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2014;4(15):13-25. Available from: https://sid.ir/paper/247371/en

IEEE: Copy

HASSAN HOZHABR, HADI MOAZED, and SAEED SHOKRIKHOOCHAK, “ESTIMATION OF REFERENCE EVAPOTRANSPIRATION (ETO) USING EMPIRICAL MODELS, ARTIFICIAL NEURAL NETWORK MODELING AND THEIR COMPARISON WITH LYSIMETER DATA IN URMIA KAHRIZI STATION,” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 4, no. 15, pp. 13–25, 2014, [Online]. Available: https://sid.ir/paper/247371/en

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