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Annual forecasting of high-temperature days in China through grey wolf optimization-based support vector machine ensemble

文献类型：期刊论文

作者Ren, Yijia4,5,6; Shi, Guowei3,6; Sun, Wei1,2,6
刊名INTERNATIONAL JOURNAL OF CLIMATOLOGY
出版日期2023-01-22
页码20
关键词ensemble learning grey wolf optimization high-temperature days support vector regression
ISSN号0899-8418
DOI10.1002/joc.7988
通讯作者Sun, Wei(sunwei29@mail.sysu.edu.cn)
英文摘要With the intensification of anthropogenic warming and urbanization, high-temperature weather poses an enormous threat to socio-economic and human healthy. However, the studies on annual high-temperature days forecasting based on machine learning are relatively deficient. This study proposes a support vector machine (SVM) ensemble model based on grey wolf optimization (GWO) to predict annual high-temperature days in Guangzhou, Shanghai and Beijing of China. Atmospheric circulation indices during 1959-2013 were utilized as inputs to train and validate models. The fivefold cross validation was used to expand the sample data and evaluate the performance of the member and ensemble models. The optimal ensemble model for Guangzhou has the highest average R (0.8939) and the lowest average root mean square error (RMSE; 3.3771), followed by the optimal ensemble models for Beijing (0.8871 and 3.6059) and Shanghai (0.7578 and 3.9968). Furthermore, compared with the typical SVM and optimal member models, the average validation RMSE of the optimal ensemble model was improved by 32.6 and 10.0% for Guangzhou, by 29.8 and 9.1% for Shanghai, and by 41.3 and 15.1% for Beijing, respectively. This study demonstrates that the GWO-based SVM ensemble model can be a promising tool for annual high-temperature days forecasting due to the nonlinear fitting power of the SVM, the hyperparameters tuning capability of the GWO algorithm, and the integration ability of ensemble learning.
WOS关键词THERMAL ENVIRONMENT ; EXTREME EVENTS ; CLIMATE-CHANGE ; PREDICTION ; MODEL ; URBANIZATION ; REGRESSION ; MORTALITY ; GUANGZHOU ; PATTERNS
资助项目Top-Notch Young Talents of Pearl River Talents Plan[2019QN01G106] ; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)[311021018] ; National Natural Science Foundation of China[51961125206] ; National Undergraduate Training Programs for Innovation and Entrepreneurship[201901048]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000918914700001
出版者WILEY
资助机构Top-Notch Young Talents of Pearl River Talents Plan ; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) ; National Natural Science Foundation of China ; National Undergraduate Training Programs for Innovation and Entrepreneurship
源URL[http://ir.igsnrr.ac.cn/handle/311030/189358]  
专题中国科学院地理科学与资源研究所
通讯作者Sun, Wei
作者单位1.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
2.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai, Guangdong, Peoples R China
3.Sun Yat sen Univ, Zhongshan Sch Med, Dept Biomed Informat, Guangzhou, Guangdong, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China
6.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China
推荐引用方式
GB/T 7714		 Ren, Yijia,Shi, Guowei,Sun, Wei. Annual forecasting of high-temperature days in China through grey wolf optimization-based support vector machine ensemble[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2023:20.
APA Ren, Yijia,Shi, Guowei,&Sun, Wei.(2023).Annual forecasting of high-temperature days in China through grey wolf optimization-based support vector machine ensemble.INTERNATIONAL JOURNAL OF CLIMATOLOGY,20.
MLA Ren, Yijia,et al."Annual forecasting of high-temperature days in China through grey wolf optimization-based support vector machine ensemble".INTERNATIONAL JOURNAL OF CLIMATOLOGY (2023):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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