ASSESSMENT OF ARTIFICIAL NEURAL NETWORKS REVENUE IN REDUCING REQUIRED PARAMETERS FOR ESTIMATION OF REFERENCE EVAPOTRANSPIRATION

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KOUCHAKZADEH M.; BAHMANI A.

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Journal: Year:2006 | Volume:11 | Issue:4 Page(s): 87-97

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Title

ASSESSMENT OF ARTIFICIAL NEURAL NETWORKS REVENUE IN REDUCING REQUIRED PARAMETERS FOR ESTIMATION OF REFERENCE EVAPOTRANSPIRATION

Author(s)

KOUCHAKZADEH M. | BAHMANI A. | Issue Writer Certificate

Keywords

EVAPOTRANSPIRATIONQ2

ARTIFICIAL NEURAL NETWORKSQ2

FAO - PENMAN MONTITHQ4

Abstract

Exact estimation of EVAPOTRANSPIRATION as a basic parameter in hydrologic cycle and study, design and management of irrigation systems is of great importance. Excessive data needed in EVAPOTRANSPIRATION equations on one hand and lack of access to some of the required data on the other hand has made problems in proper computation of this parameter in some areas. This study aims at evaluating ARTIFICIAL NEURAL NETWORKS revenue in reducing required data for estimation of reference EVAPOTRANSPIRATION as well as its comparison with experimental methods of FAO-Penman Montith, Blany-Kridel, refined method of Jensen-Haize and Hargrives-Samani. To achieve this objective perceptron multilayer networks with learning law of back propagation error and daily data of Tehran Mehrabad weather station during 1991- 2000 were used. FAO-Penman Montith was selected as the standard method and 11 ANN model with different structures were designed with the parameters (Tmax, Tmin, Tdew, Rn, RHmax, U2, n). Favorite network was selected based on (RMSE, R2, MAE, MBE) criteria. Results showed that ANN 1 model with (Tmean, Rn, U2, RHmean) as input data and ANN10 model with only one input, (Tmean) were the most and least precise models in evaluation of EVAPOTRANSPIRATION respectively. Air temperatue and wind velocity were considered to be the two most effective data in models precision. Although reducing the number of input data in ANN models will results in the reduction of their output precision but results showed that ANN models can be used as a useful tool for ET estimation in presence of the methods such as FAO-Penman Montith, Blany-Kridel, refined method of Jensen-Haize and Hargrives-Samani.

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APA: Copy

KOUCHAKZADEH, M., & BAHMANI, A.. (2006). ASSESSMENT OF ARTIFICIAL NEURAL NETWORKS REVENUE IN REDUCING REQUIRED PARAMETERS FOR ESTIMATION OF REFERENCE EVAPOTRANSPIRATION. JOURNAL OF AGRICULTURAL SCIENCES, 11(4), 87-97. SID. https://sid.ir/paper/400662/en

Vancouver: Copy

KOUCHAKZADEH M., BAHMANI A.. ASSESSMENT OF ARTIFICIAL NEURAL NETWORKS REVENUE IN REDUCING REQUIRED PARAMETERS FOR ESTIMATION OF REFERENCE EVAPOTRANSPIRATION. JOURNAL OF AGRICULTURAL SCIENCES[Internet]. 2006;11(4):87-97. Available from: https://sid.ir/paper/400662/en

IEEE: Copy

M. KOUCHAKZADEH, and A. BAHMANI, “ASSESSMENT OF ARTIFICIAL NEURAL NETWORKS REVENUE IN REDUCING REQUIRED PARAMETERS FOR ESTIMATION OF REFERENCE EVAPOTRANSPIRATION,” JOURNAL OF AGRICULTURAL SCIENCES, vol. 11, no. 4, pp. 87–97, 2006, [Online]. Available: https://sid.ir/paper/400662/en

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