EVALUATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND IRMAK EXPERIMENTAL MODELS TO PREDICT DAILY SOLAR NET RADIATION (RN) IN COLD SEMI-ARID CLIMATE (CASE STUDY: HAMEDAN)

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.

SABZIPARVAR A.A.; KHATAAR B.

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

Journal Paper

Paper Information

Journal: WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE) Year:2015 | Volume:25 | Issue:2 Page(s): 37-50

Download Full-Text

Persian Verion

View:

781

Download:

Cites:

Information Journal Paper

Title

EVALUATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND IRMAK EXPERIMENTAL MODELS TO PREDICT DAILY SOLAR NET RADIATION (RN) IN COLD SEMI-ARID CLIMATE (CASE STUDY: HAMEDAN)

Author(s)

SABZIPARVAR A.A. | KHATAAR B. | Issue Writer Certificate

Keywords

ANNQ2

ESTIMATIONQ2

IRMAK MODELQ3

SOLAR NET RADIATION LOGGERQ3

Abstract

Solar net radiation (Rn) is one of the most important factors influencing soil heat flux and evapotranspiration rate process. This parameter is derived from the difference between downward and upward radiation fluxes reaching the earth’s surface. Field measurements of Rn are cost effective and difficult to maintain. Therefore, in the most cases, Rn is estimated by empirical, semiempirical and physical-based models. Recent studies show that the artificial neural network (ANN) is a reliable tool for estimating daily Rn with reasonable performance for the area where lack or shortage of field Rn exists. Using IRMAK MODEL and ANN approach, we tried to estimate daily Rn for one of the cold semi-arid sites located in Hamedan. For model evaluations, Rn data were measured in hourly base during December 2011 to December 2012 at Bu-Ali Sina University weather site. In this study, we used 11 daily meteorological parameters as the inputs of ANN to generate the Rn estimates (70% of the data set for training data and 30% for model validation). The results showed that the best model performance of ANN was obtained from a 11-2-1 architecture and the sigmoid function based on the back- propagation training algorithm. The least ANN error was observed by employing 10000 iterations for the training step and two neurons in the hidden layers. The results indicated that the daily net radiation from ANN was more accurate (R2>0.95) than the previously recommended IRMAK MODEL.

Cites

No record.

References

No record.

Cite

APA: Copy

SABZIPARVAR, A.A., & KHATAAR, B.. (2015). EVALUATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND IRMAK EXPERIMENTAL MODELS TO PREDICT DAILY SOLAR NET RADIATION (R_N) IN COLD SEMI-ARID CLIMATE (CASE STUDY: HAMEDAN). WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), 25(2), 37-50. SID. https://sid.ir/paper/147725/en

Vancouver: Copy

SABZIPARVAR A.A., KHATAAR B.. EVALUATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND IRMAK EXPERIMENTAL MODELS TO PREDICT DAILY SOLAR NET RADIATION (R_N) IN COLD SEMI-ARID CLIMATE (CASE STUDY: HAMEDAN). WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE)[Internet]. 2015;25(2):37-50. Available from: https://sid.ir/paper/147725/en

IEEE: Copy

A.A. SABZIPARVAR, and B. KHATAAR, “EVALUATION OF ARTIFICIAL NEURAL NETWORK (ANN) AND IRMAK EXPERIMENTAL MODELS TO PREDICT DAILY SOLAR NET RADIATION (R_N) IN COLD SEMI-ARID CLIMATE (CASE STUDY: HAMEDAN),” WATER AND SOIL SCIENCE (AGRICULTURAL SCIENCE), vol. 25, no. 2, pp. 37–50, 2015, [Online]. Available: https://sid.ir/paper/147725/en

Related Journal Papers