Volume 13, Issue 50 (winter 2010)
jwss 2010, 13(50): 53-62 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rahimi Khob A, Behbahani M, Jamshidi M. Evaluation of Two Empirical Methods and Artificial Neural Network Models Used for Estimation of Solar Radiation Intercepted at the Earth’s Surface: A Case Study in Southeast of Tehran. jwss 2010; 13 (50) :53-62
URL: http://jstnar.iut.ac.ir/article-1-1186-en.html
URL: http://jstnar.iut.ac.ir/article-1-1186-en.html
, akhob@ut.ac.ir
Abstract: (22944 Views)
Daily solar radiation intercepted at the earth’s surface is an input required for water resources, environmental and agricultural studies. However, the measurement of this parameter can only be done in a few places. This has led researchers to develop a number of methods for estimating solar radiation based on frequently available meteorological records such as hours of sunshine or air temperature. In this study two empirical Angestrom and Hargreaves- Samani models, which are respectively based on air temperature and sunshine duration were calibrated and evaluated for estimating solar radiation in southeast of Tehran, Iran. Also, two neural networks models were presented using similar inputs and above-mentioned empirical models. The results showed that the both empirical and neural network models provided closer agreement with the measured values, but the models based on sunshine hours gave better estimates than the models based on air temperature. The neural network model based on sunshine hours with a R2 of 0.97 and a RMSE of 1.34 MJ m-2 d-1 provided the best results
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
