干旱区生态环境知识资源中心(Arid): Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China

Arid

DOI	10.2166/wst.2023.162
	Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China
	Li, Shaoxuan; Xie, Jiancang; Yang, Xue; Jing, Xin
通讯作者	Yang, X
来源期刊	WATER SCIENCE AND TECHNOLOGY
ISSN	0273-1223
EISSN	1996-9732
出版年	2023
卷号	87 期号:11 页码:2756-2775
英文摘要	Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not sufficient enough, even though the general performance is acceptable. Therefore, the scholars tried the signal decomposition algorithm as a data pre-processing tool, and coupled it with the stand-alone model to build 'decomposition-prediction' model to improve the performance. Considering the limitations of using the single decomposition algorithm, an 'integration-prediction' model construction method is proposed in this study, which deeply combines the results of multiple decomposition algorithms. The model tested three meteorological stations in Guanzhong, Shaanxi Province, China, where the short-term meteorological drought is predicted from 1960 to 2019. The meteorological drought index selects the Standardized Precipitation Index on a 12-month time scale (SPI-12). Compared with stand-alone models and 'decomposition-prediction' models, the 'integration-prediction' models present higher prediction accuracy, smaller prediction error and better stability in the results. This new 'integration-prediction' model provides attractive value for drought risk management in arid regions.
英文关键词	'integration-prediction' model machine learning meteorological drought prediction signal decomposition algorithm SPI
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:000994926900001
WOS关键词	SUPPORT VECTOR REGRESSION ; DECOMPOSITION ; DISTRICT ; BASIN ; SPI
WOS类目	Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399161
推荐引用方式 GB/T 7714	Li, Shaoxuan,Xie, Jiancang,Yang, Xue,et al. Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China[J],2023,87(11):2756-2775.
APA	Li, Shaoxuan,Xie, Jiancang,Yang, Xue,&Jing, Xin.(2023).Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China.WATER SCIENCE AND TECHNOLOGY,87(11),2756-2775.
MLA	Li, Shaoxuan,et al."Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China".WATER SCIENCE AND TECHNOLOGY 87.11(2023):2756-2775.