A quantitative model based on grey theory for sea surface temperature prediction - Meng, Fanyu; Gu, Jilin; Wang, Ling-en; Qin, Zhibin; Gao, Mingyao; Chen, Junhong; Li, Xueming (Frontiers in Environmental Science, 2022)
In order to predict sea surface temperature (SST) , combined with the genetic algorithm and the least square method, a GM(1,1|sin) power model prediction method based on similarity deviation is proposed. We firstly combined the data of two consecutive years into a new time series, analyzes the similarity of the data of the previous year, and obtains the most similar year and the corresponding new time series. Then we established 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) was 9.81% and 1.0627respectively. All of evaluation metrics of the 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 is 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 MRE. It proves that this proposed model can be regarded as a better solution to predicting SST.