Predictive Modeling of Global Temperature Change Based on Artificial Intelligence Algorithms - Zhang, Jincheng; Sun, Shichang; Wang, Runqing (International Journal of Computer Science and Information Technology, 2024)
As the greenhouse effect intensifies, global temperature changes are increasingly affecting all aspects of economic life. The purpose of this paper is to analyze the influencing factors of temperature to establish an analytical model of global temperature change. The Berkeley Earth Climate Database provides access to the data needed for the study. Pearson correlation analysis was used to quantify the strength of the correlations between temperature, location and other factors. After that, global temperatures were modelled and predicted by artificial intelligence algorithms such as ARIMA and linear regression prediction models, respectively. The results test that the model has good stability and can achieve accurate prediction function. The model can better help predict and respond to global temperature changes. The application of this AI algorithm can be used as a reference in other fields.