ESTIMATION OF REFERENCE EVAPOTRANSPIRATION IN GREENHOUSE BY ARTIFICIAL NEURAL NETWORK

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ASSARI M.; KOUCHAKZADEH M.; SHAHABIFAR M.; BAYAT K.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Water and Soil Conservation Year:2009 | Volume:16 | Issue:1 Page(s): 107-121

Download Full-Text

Persian Verion

View:

1,060

Download:

Cites:

Information Journal Paper

Title

ESTIMATION OF REFERENCE EVAPOTRANSPIRATION IN GREENHOUSE BY ARTIFICIAL NEURAL NETWORK

Author(s)

ASSARI M. | KOUCHAKZADEH M. | SHAHABIFAR M. | BAYAT K. | Issue Writer Certificate

Keywords

REFERENCE EVAPOTRANSPIRATIONQ3

ARTIFICIAL NEURAL NETWORKQ2

GREENHOUSEQ3

Abstract

Nowadays ARTIFICIAL NEURAL NETWORKs (ANNs) are being applied in several problems of water engineering where there is no clear relationship between effective parameters on the estimation of phenomenon. This research was used to measure aerodynamic data inside and outside GREENHOUSE for estimating REFERENCE EVAPOTRANSPIRATION in GREENHOUSE by using ANNs. ANN was used with perceptron multilayer structure and Back Propagation with one hidden layer for estimating evapotranspiration by using meteorological parameters. Results showed, with regard to Root Mean Square Error (RMSE), ANNs wasable to estimate REFERENCE EVAPOTRANSPIRATION with low error. Inside GREENHOUSE, ANN showed a best estimation maximum temperature (Tmax), minimum temperature (Tmin), extraterrestrial radiation (Ra), actual vapor pressure (ea) and sunshine (n) in entrance layer and found as the best model for estimating inside GREENHOUSE REFERENCE EVAPOTRANSPIRATION with RMSE equal to 1.1 mm day-1. Outside GREENHOUSE, ANN was found as best model which can use maximum temperature (Tmax), minimum temperature (Tmin), and sunshine (n) in entrance layer estimating inside GREENHOUSE REFERENCE EVAPOTRANSPIRATION with RMSE equal to 1.01 mm day-1.

Cites

No record.

References

No record.

Cite

APA: Copy

ASSARI, M., KOUCHAKZADEH, M., SHAHABIFAR, M., & BAYAT, K.. (2009). ESTIMATION OF REFERENCE EVAPOTRANSPIRATION IN GREENHOUSE BY ARTIFICIAL NEURAL NETWORK. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 16(1), 107-121. SID. https://sid.ir/paper/156493/en

Vancouver: Copy

ASSARI M., KOUCHAKZADEH M., SHAHABIFAR M., BAYAT K.. ESTIMATION OF REFERENCE EVAPOTRANSPIRATION IN GREENHOUSE BY ARTIFICIAL NEURAL NETWORK. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2009;16(1):107-121. Available from: https://sid.ir/paper/156493/en

IEEE: Copy

M. ASSARI, M. KOUCHAKZADEH, M. SHAHABIFAR, and K. BAYAT, “ESTIMATION OF REFERENCE EVAPOTRANSPIRATION IN GREENHOUSE BY ARTIFICIAL NEURAL NETWORK,” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 16, no. 1, pp. 107–121, 2009, [Online]. Available: https://sid.ir/paper/156493/en

Related Journal Papers