Predictive Modeling of Global Temperature Change Based on Artificial Intelligence Algorithms

Jincheng Zhang; Shichang Sun; Runqing Wang

doi:10.62051/ijcsit.v4n1.35

Authors

Jincheng Zhang
Shichang Sun
Runqing Wang

DOI:

https://doi.org/10.62051/ijcsit.v4n1.35

Keywords:

Global Warming, ARIMA Model, Linear Regression Models, Artificial Intelligence (AI)

Abstract

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.

Downloads

Download data is not yet available.

References

[1] Loiy Al Ghussain. "Global warming: review on driving forces and mitigation." Environmental Progress & Sustainable Energy 38.1(2019). doi:10.1002/ep.13041.

[2] Qin Li, Zhou Xin. Possible impact of human activities on temperature change in Beijing area since the industrial revolution [J] 2010, 55(06):522-525.

[3] Xiao Yuanjun, Li Baoshan, Song Wendan, Cheng Yongxiang, Huang Jingfeng. Effects of PM (2.5) concentration on temperature in China from 1998 to 2016 [J]. Journal of Meteorology and Environment, 2022, 38(03):85-92.

[4] Wu Chunde, Sun Yankun. Heilongjiang Meteorology,2014,31(01):39-41. DOI:10.14021/j.cnki.hljqx.2014.01.019.

[5] Li-Ping Tian, et al. "Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data." The Scientific World Journal 2014. (2014). doi:10.1155/2014/313747.

[6] Liu Fang, GE Ruiting. Prediction and analysis of winter minimum temperature based on ARIMA [J]. Electronic Technology and Software Engineering, 2022(12):184-188.

[7] Zhou Suiyuan, XU Xin, XU Xiaoqian. Model analysis of carbon dioxide demand prediction based on univariate linear regression method [J]. Guangdong Chemical Industry, 2020, 47(23):64-66+75.

[8] Cheng Liu and Yixiao Sun. "A simple and trustworthy asymptotic t test in difference-in-differences regressions." Journal of Econometrics 210.2(2019). doi:10.1016/j.jeconom.2019.02.003.