干旱区生态环境知识资源中心(Arid): Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model

Arid

DOI	10.1016/j.jhydrol.2024.131805
	Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model
	Su, Tao; Liu, Dan; Cui, Xingyuan; Dou, Xianshen; Lei, Bo; Cheng, Xu; Yuan, Mengning; Chen, Renjie
通讯作者	Su, T
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2024
卷号	641
英文摘要	Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance.
英文关键词	Drought prediction VMD LSTM VMD-CBiLSTM Mixed model
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001297047800001
WOS关键词	CHINA ; DECOMPOSITION ; PERFORMANCE ; OPTIMIZATION ; ALGORITHM ; SELECTION
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404606
推荐引用方式 GB/T 7714	Su, Tao,Liu, Dan,Cui, Xingyuan,et al. Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model[J],2024,641.
APA	Su, Tao.,Liu, Dan.,Cui, Xingyuan.,Dou, Xianshen.,Lei, Bo.,...&Chen, Renjie.(2024).Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model.JOURNAL OF HYDROLOGY,641.
MLA	Su, Tao,et al."Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model".JOURNAL OF HYDROLOGY 641(2024).