干旱区生态环境知识资源中心(Arid): Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China

Arid

DOI	10.2166/nh.2022.115
	Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China
	Li Shaoxuan; Xie Jiancang; Yang Xue; Xue Ruihua; Zhao Peiyuan
通讯作者	Yang, X
来源期刊	HYDROLOGY RESEARCH
ISSN	1998-9563
EISSN	2224-7955
出版年	2022
卷号	53 期号:3 页码:419-440
英文摘要	Drought prediction plays an important guiding role in drought risk management. The standardized precipitation index (SPI) is a popular meteorological drought indicator to measure the degree of drought. The SPI time series is non-stationary, whereas the conventional artificial neural network (ANN) model has limitations to predict non-stationary time series. To overcome this limitation, it is essential to investigate input data preprocessing to improve the ANN model. In this paper, a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed (SSA-BP-ANN). The advantage of this model is that the SSA of finite-length SPI sequences does not require the adoption of boundary extensions to suppress boundary effects, while the most predictable components of the SPI can be efficiently extracted and incorporated into the model. The proposed SSA-BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that the SSA-BP-ANN model can produce more accurate predictions than the BP-ANN model. In addition, the performance improvement of SSA on the BP-ANN model is slightly better than wavelet decomposition and empirical mode decomposition. This new hybrid prediction model has great potential for promoting drought early warning in arid regions.
英文关键词	BP artificial neural network data preprocessing decomposition hybrid model singular spectrum analysis
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000780122200005
WOS关键词	SINGULAR SPECTRUM ANALYSIS ; SUPPORT VECTOR REGRESSION ; STANDARDIZED PRECIPITATION INDEX ; RIVER-BASIN ; WAVELET TRANSFORM ; DROUGHT ; DECOMPOSITION ; SPEI
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393063
推荐引用方式 GB/T 7714	Li Shaoxuan,Xie Jiancang,Yang Xue,et al. Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China[J],2022,53(3):419-440.
APA	Li Shaoxuan,Xie Jiancang,Yang Xue,Xue Ruihua,&Zhao Peiyuan.(2022).Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China.HYDROLOGY RESEARCH,53(3),419-440.
MLA	Li Shaoxuan,et al."Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China".HYDROLOGY RESEARCH 53.3(2022):419-440.