A quantitative model based on grey theory for sea surface temperature prediction
文献类型：期刊论文
| 作者 | Meng, Fanyu4; Gu, Jilin3,4; Wang, Ling-en1; Qin, Zhibin4; Gao, Mingyao2; Chen, Junhong4; Li, Xueming3 |
| 刊名 | FRONTIERS IN ENVIRONMENTAL SCIENCE
 |
| 出版日期 | 2022-11-10 |
| 卷号 | 10页码:17 |
| 关键词 | sea surface temperature grey theory GM(11|sin) power model genetic algorithm prediction |
| DOI | 10.3389/fenvs.2022.1014856 |
| 通讯作者 | Gu, Jilin(gujilin@lnnu.edu.cn) ; Wang, Ling-en(wangle@igsnrr.ac.cn) |
| 英文摘要 | In order to predict sea surface temperature (SST), combined with the genetic algorithm and the least-squares method, a GM(1,1|sin) power model prediction method based on similarity deviation is proposed. We first combined the data of two consecutive years into a new time series, analyzed the similarity of the data of the previous year, and obtained the most similar year and the corresponding new time series. Then, we established a GM(1,1|sin) power model to predict SST. In model validation, we predicted the monthly average SST from 2016 to 2020 with the data from 1985 to 2015, 2016, 2017, 2018, and 2019. The validation results showed that the maximum mean relative error (MRE) was 13.28%, the minimum MRE was 5.54%, and the average MRE and the root mean square error (RMSE) were 9.81% and 1.0627, respectively. All of evaluation metrics of Lin's concordance correlation coefficient (LCCC) and the ratio of performance to deviation (RPD) were excellent. We iteratively predicted the monthly average SST from 2016 to 2020 with the data from 1985 to 2015, the maximum MRE was 13.91%, the minimum was 7.80%, and the average MRE, RMSE, LCCC and RPD are 11.07% 1.0603, 0.9894, and 7.497, respectively. Compared with GM(1,1), GM(1,1|sin + cos), and GM(1,1|sin) models, the proposed model outperformed these models with at least 50% in the MRE. It proves that the proposed model can be regarded as a better solution to predicting SST. |
| 资助项目 | National Natural Science Foundation of China ; Foundation of Liaoning Educational Committee[41671158] ; College Students' Innovative Entrepreneurial Training Plan Program[LJKZ0979] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[S202110165051] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000890689000001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | National Natural Science Foundation of China ; Foundation of Liaoning Educational Committee ; College Students' Innovative Entrepreneurial Training Plan Program ; Youth Innovation Promotion Association of Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187628]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Gu, Jilin; Wang, Ling-en |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Liaoning Normal Univ, Sch Foreign Languages, Dalian, Peoples R China 3.Liaoning Normal Univ, Sch Geog, Dalian, Peoples R China 4.Liaoning Normal Univ, Sch Phys & Elect Technol, Dalian, Peoples R China |
| 推荐引用方式 GB/T 7714 | Meng, Fanyu,Gu, Jilin,Wang, Ling-en,et al. A quantitative model based on grey theory for sea surface temperature prediction[J]. FRONTIERS IN ENVIRONMENTAL SCIENCE,2022,10:17. |
| APA | Meng, Fanyu.,Gu, Jilin.,Wang, Ling-en.,Qin, Zhibin.,Gao, Mingyao.,...&Li, Xueming.(2022).A quantitative model based on grey theory for sea surface temperature prediction.FRONTIERS IN ENVIRONMENTAL SCIENCE,10,17. |
| MLA | Meng, Fanyu,et al."A quantitative model based on grey theory for sea surface temperature prediction".FRONTIERS IN ENVIRONMENTAL SCIENCE 10(2022):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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