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DOI | 10.1002/joc.4762 |
Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree | |
Wang, Lunche1; Kisi, Ozgur2; Zounemat-Kermani, Mohammad3; Zhu, Zhongmin4,5; Gong, Wei5,6; Niu, Zigeng1; Liu, Hongfu1; Liu, Zhengjia7 | |
2017-03-15 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Turkey; Iran |
英文摘要 | Solar radiation is one of the major factors for agricultural, meteorological and ecological applications. In this study, two different optimized adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and M5Tree (M5Tree) methods are proposed for modelling daily global solar radiation (G). Daily meteorological variables at 21 stations in China are used for training and testing the applied models, which is evaluated through root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R-2). Above models will be compared with a calibrated empirical angstrom ngstrom model and the results indicate that the ANFIS models provide better accuracy than the M5Tree and empirical method, for example, the RMSE values for ANFIS-SC, ANFIS-GP, M5Tree and the angstrom ngstrom model range 2.10-3.08, 2.07-3.08, 2.79-3.87 and 2.54-3.69MJm(-2)day(-1), respectively. The model performances also show some differences at different stations for each model, for example, the ANFIS models produce the most accurate estimations at station 58238, while M5Tree brings the best accuracy at the station 51777. Meanwhile, the models underestimate high radiation values for some stations, which may due to the differences in training and testing data ranges and distribution of the stations. Finally, the reasons for the differences in model performance are investigated in detail. |
英文关键词 | global solar radiation adaptive neuro-fuzzy inference system grid partitioning substructive clustering M5Tree China |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000395349500002 |
WOS关键词 | INFERENCE SYSTEM ; COMPUTING TECHNIQUE ; GLOBAL RADIATION ; NETWORK ; SURFACE ; TEMPERATURE ; IRRADIATION ; REGRESSION ; INCIDENT ; ENERGY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/36697 |
专题 | 气候变化 |
作者单位 | 1.China Univ Geosci, Sch Earth Sci, Lab Crit Zone Evolut, 388 Lumo Rd, Wuhan 430074, Peoples R China; 2.Canik Basari Univ, Fac Engn & Architecture, Dept Civil Engn, Samsun, Turkey; 3.Shahid Bahonar Univ Kerman, Dept Water Engn, Kerman, Iran; 4.Huazhong Univ Sci & Technol, Wuchang Branch, Wuhan, Peoples R China; 5.Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan, Peoples R China; 6.Collaborat Innovat Ctr Geospatial Technol, Wuhan, Peoples R China; 7.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Lunche,Kisi, Ozgur,Zounemat-Kermani, Mohammad,et al. Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2017,37(3). |
APA | Wang, Lunche.,Kisi, Ozgur.,Zounemat-Kermani, Mohammad.,Zhu, Zhongmin.,Gong, Wei.,...&Liu, Zhengjia.(2017).Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37(3). |
MLA | Wang, Lunche,et al."Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37.3(2017). |
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