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DOI | 10.1002/joc.6037 |
Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches | |
Maroufpoor, Saman1; Sanikhani, Hadi2; Kisi, Ozgur3; Deo, Ravinesh C.4,5; Yaseen, Zaher Mundher6 | |
2019-06-30 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2019 |
卷号 | 39期号:8页码:3543-3557 |
文章类型 | Article |
语种 | 英语 |
国家 | Iran; Georgia; Australia; Vietnam |
英文摘要 | Wind speed is an essential component that needs to be determined accurately, especially over long-term periods for various engineering and scientific purposes including renewable energy productions, structural building sustainability and others. In this study, six different heuristic methods: multi-layer perceptron artificial neural networks, (ANN), adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), generalized regression neural networks (GRNN), gene expression programming (GEP) and multivariate adaptive regression spline (MARS) are developed to model monthly wind speeds using meteorological input information. The atmospheric pressure, temperature, relative humidity and rainfall values are obtained from Jolfa and Tabriz meteorological stations, Iran, and are used to build the proposed predictive models.. Different statistical indicators are computed to evaluate and comprehensively assess the performance of the six heuristic methods. Over the testing phase, the ANFIS-GP and GRNN models are seen to exhibit the highest predictive performance for the Jolfa and Tabriz stations, respectively. That is, the maximum coefficient of determination are found to be 0.874, 0.858, 0.850, 0.849, 0.847 and 0.826, for the GRNN, ANFIS-GP, ANFIS-SC, ANN, GEP and MARS models, respectively, for Jolfa station, respectively, revealing the superiority of GRNN over the five counterpart models. The results show the generalization capability of the tested heuristic artificial intelligence techniques for both study stations, and therefore could be explored for windspeed prediction and various decisions made in regards to climate change studies. |
英文关键词 | gene expression programming multivariate adaptive regression spline neural networks neuro-fuzzy prediction wind speed |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000474160800009 |
WOS关键词 | NEURAL-NETWORKS ; ALGORITHM |
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/184309 |
专题 | 气候变化 |
作者单位 | 1.Univ Tehran, Irrigat & Reclamat Engn Dept, Fac Agr, Tehran, Iran; 2.Univ Kurdistan, Water Engn Dept, Fac Agr, Sanandaj, Iran; 3.Ilia State Univ, Fac Nat Sci & Engn, Tbilisi 0162, Georgia; 4.Univ Southern Queensland, Ctr Sustainable Agr Syst, Sch Agr Computat & Environm Sci, Springfield Cent, Qld, Australia; 5.Univ Southern Queensland, Ctr Appl Climate Sci, Sch Agr Computat & Environm Sci, Springfield Cent, Qld, Australia; 6.Ton Duc Thang Univ, Sustainable Dev Civil Engn Res Grp, Fac Civil Engn, Ho Chi Minh City, Vietnam |
推荐引用方式 GB/T 7714 | Maroufpoor, Saman,Sanikhani, Hadi,Kisi, Ozgur,et al. Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2019,39(8):3543-3557. |
APA | Maroufpoor, Saman,Sanikhani, Hadi,Kisi, Ozgur,Deo, Ravinesh C.,&Yaseen, Zaher Mundher.(2019).Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches.INTERNATIONAL JOURNAL OF CLIMATOLOGY,39(8),3543-3557. |
MLA | Maroufpoor, Saman,et al."Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches".INTERNATIONAL JOURNAL OF CLIMATOLOGY 39.8(2019):3543-3557. |
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