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

Abstract

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.

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