EVALUATING ARTIFICIAL NEURAL NETWORK AND PHYSICAL-EMPIRICAL MODELS OF REFERENCE CROP EVAPOTRANSPIRATION ESTIMATION IN SEMI-ARID CLIMATE

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.

FAGHIH H.

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

Journal Paper

Paper Information

Journal: Water and Soil Science Year:2015 | Volume:25 | Issue:4/2 Page(s): 137-152

Download Full-Text

Persian Verion

View:

662

Download:

Cites:

Information Journal Paper

Title

EVALUATING ARTIFICIAL NEURAL NETWORK AND PHYSICAL-EMPIRICAL MODELS OF REFERENCE CROP EVAPOTRANSPIRATION ESTIMATION IN SEMI-ARID CLIMATE

Author(s)

FAGHIH H. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ3

EVAPOTRANSPIRATIONQ2

LYSIMETERQ2

SEMI-ARID CLIMATEQ2

Abstract

Accurate estimation of EVAPOTRANSPIRATION (ET), which is one of the main components of hydrologic cycle, is very important in water resources management, irrigation planning and environmental studies. Accurate measurement of this component is very difficult, so the application of the models relying on available meteorological variables might be an alternative to direct measurement. This study aimed at evaluating multilayer perceptron ARTIFICIAL NEURAL NETWORK as well as sixteen reference crop ET estimation models in Sanandaj. To achieve this object, LYSIMETERic ET values were considered as standard values for evaluating the applied models. The results showed that the FAO improved pan evaporation method provides more accurate results than other applied methods. In addition, the results indicated that the ARTIFICIAL NEURAL NETWORK produces more accurate results for estimating reference EVAPOTRANSPIRATION (ET0). Among others, the pan evaporation and ARTIFICIAL NEURAL NETWORK models showed, respectively, 0.28% and 3.37% under-estimation and overestimation in the studied region.

Cites

No record.

References

No record.

Cite

APA: Copy

FAGHIH, H.. (2015). EVALUATING ARTIFICIAL NEURAL NETWORK AND PHYSICAL-EMPIRICAL MODELS OF REFERENCE CROP EVAPOTRANSPIRATION ESTIMATION IN SEMI-ARID CLIMATE. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), 25(4/2), 137-152. SID. https://sid.ir/paper/147850/en

Vancouver: Copy

FAGHIH H.. EVALUATING ARTIFICIAL NEURAL NETWORK AND PHYSICAL-EMPIRICAL MODELS OF REFERENCE CROP EVAPOTRANSPIRATION ESTIMATION IN SEMI-ARID CLIMATE. WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE)[Internet]. 2015;25(4/2):137-152. Available from: https://sid.ir/paper/147850/en

IEEE: Copy

H. FAGHIH, “EVALUATING ARTIFICIAL NEURAL NETWORK AND PHYSICAL-EMPIRICAL MODELS OF REFERENCE CROP EVAPOTRANSPIRATION ESTIMATION IN SEMI-ARID CLIMATE,” WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), vol. 25, no. 4/2, pp. 137–152, 2015, [Online]. Available: https://sid.ir/paper/147850/en

Related Journal Papers