干旱区生态环境知识资源中心(Arid): A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN

Arid

DOI	10.1007/s00521-018-3916-0
	A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN
	Bayatvarkeshi, Maryam 1; Mohammadi, Kourosh 2; Kisi, Ozgur 3; Fasihi, Rojin 1
通讯作者	Kisi, Ozgur
来源期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
EISSN	1433-3058
出版年	2020
卷号	32 期号:9 页码:4989-5000
英文摘要	Relative humidity (RH) has an important effect on precipitation, especially in arid and semiarid regions. Prediction of RH has been the focus of attention of climate change researchers as well. In this investigation, the accuracy of six intelligent models, including an artificial neural network (ANN), a co-active neuro-fuzzy inference system (CANFIS), principal component analysis (PCA) combined with ANN (PCA-ANN) and three hybrid wavelet-artificial intelligence models, including WANN, WCANFIS and WPCA-ANN, was evaluated in daily RH prediction. Thirty weather stations located in different climates in Iran for the period 2000-2010 were selected for the evaluation and comparison of these models. The performance of each model was evaluated using correlation coefficient (r) and normal root mean square error (NRMSE). Based on the statistical evaluation criteria, the accuracy ranks of the six models were: WPCA-ANN, WCANFIS, WANN, PCA-ANN, ANN and CANFIS. The results indicated that the WPCA-ANN model was the optimal model for estimation of RH, and the range of NRMSE and r values were from 0.009 to 0.080 and from 0.996 to 0.999, respectively. Overall, WPCA-ANN is a new approach that can be successfully applied to predict RH with a high accuracy.
英文关键词	Relative humidity Estimation Principal component analysis Wavelet transform ANN
类型	Article
语种	英语
国家	Iran ; Canada ; Georgia
收录类别	SCI-E
WOS记录号	WOS:000527419900055
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; AIR-TEMPERATURE ; PREDICTION ; PCA
WOS类目	Computer Science, Artificial Intelligence
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315229
作者单位	1.Malayer Univ, Agr Fac, Malayer, Iran; 2.HLV2 K Engn, Brampton, ON, Canada; 3.Ilia State Univ, Fac Nat Sci & Engn, Tbilisi, Georgia
推荐引用方式 GB/T 7714	Bayatvarkeshi, Maryam,Mohammadi, Kourosh,Kisi, Ozgur,et al. A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN[J],2020,32(9):4989-5000.
APA	Bayatvarkeshi, Maryam,Mohammadi, Kourosh,Kisi, Ozgur,&Fasihi, Rojin.(2020).A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN.NEURAL COMPUTING & APPLICATIONS,32(9),4989-5000.
MLA	Bayatvarkeshi, Maryam,et al."A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN".NEURAL COMPUTING & APPLICATIONS 32.9(2020):4989-5000.