Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1002/joc.3441 |
Comparative study of statistical and artificial neural network’s methodologies for deriving global solar radiation from NOAA satellite images | |
Rahimikhoob, A.; Behbahani, S. M. R.; Banihabib, M. E. | |
通讯作者 | Rahimikhoob, A. |
来源期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2013 |
卷号 | 33期号:2页码:480-486 |
英文摘要 | The use of satellite data to estimate global solar radiation (G) at ground level has become an effective way for a large area with high spatial and temporal resolution. The statistical approach is a widely applied procedure for this task. The first objective of this study was to examine the potential of this approach for deriving instantaneous G from NOAAAVHRR satellite data for the atmosphere of semi-arid environment of Iran. The second objective was to apply artificial neural network (ANN) to the estimation of G from advanced very high resolution radiometer (AVHRR) images. A Comparison between these two methods was the last objective of this study. A total of 661 images of NOAAAVHRR level 1b, covering the area of this study were collected from the Satellite Active Archive of NOAA. The results demonstrated that the use of ANN model gave better estimates than the statistical technique. Root mean square error and R2 for the comparison between observed and estimated G for the tested data using the proposed ANN model are 56.95 W m-2 and 0.90, respectively. For the statistical approach method these values are 68.33 W m-2 and 0.86. Copyright (C) 2012 Royal Meteorological Society |
英文关键词 | global solar radiation artificial neural network statistical approach AVHRR data Iran |
类型 | Article |
语种 | 英语 |
国家 | Iran |
收录类别 | SCI-E |
WOS记录号 | WOS:000313753900017 |
WOS关键词 | FEEDFORWARD NETWORKS ; TEMPERATURE ; MODEL ; IRRADIANCE ; VALIDATION ; ALGORITHM |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/177694 |
作者单位 | Univ Tehran, Dept Irrigat & Drainage Engn, Tehran, Iran |
推荐引用方式 GB/T 7714 | Rahimikhoob, A.,Behbahani, S. M. R.,Banihabib, M. E.. Comparative study of statistical and artificial neural network’s methodologies for deriving global solar radiation from NOAA satellite images[J],2013,33(2):480-486. |
APA | Rahimikhoob, A.,Behbahani, S. M. R.,&Banihabib, M. E..(2013).Comparative study of statistical and artificial neural network’s methodologies for deriving global solar radiation from NOAA satellite images.INTERNATIONAL JOURNAL OF CLIMATOLOGY,33(2),480-486. |
MLA | Rahimikhoob, A.,et al."Comparative study of statistical and artificial neural network’s methodologies for deriving global solar radiation from NOAA satellite images".INTERNATIONAL JOURNAL OF CLIMATOLOGY 33.2(2013):480-486. |
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