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	Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
	Masrur Ahmed, A.A.1; Deo, Ravinesh C.1; Feng, Qi2,3 ; Ghahramani, Afshin 4; Raj, Nawin 1; Yin, Zhenliang 2,3; Yang, Linshan 2,3
发表日期	2021
卷号	v 599
语种	英语
来源期刊	Journal of Hydrology
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/239853
作者单位	1.School of Sciences, University of Southern Queensland, Springfield; QLD; 4300, Australia 2.Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, China 3.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd 320, Lanzhou; Gansu Province; 730000, China 4.Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba; QLD; 4500, Australia
推荐引用方式 GB/T 7714	Masrur Ahmed, A.A.,Deo, Ravinesh C.,Feng, Qi,et al. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity[J]. 中国科学院西北生态环境资源研究院,2021,v 599.
APA	Masrur Ahmed, A.A..,Deo, Ravinesh C..,Feng, Qi.,Ghahramani, Afshin.,Raj, Nawin.,...&Yang, Linshan.(2021).Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity.Journal of Hydrology,v 599.
MLA	Masrur Ahmed, A.A.,et al."Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity".Journal of Hydrology v 599(2021).