The World Temperature Changes Related to Atmospheric Concentrations in the 21st Century Based on Machine Learning

DOI

10.2991/aebmr.k.220405.051How to use a DOI?

Keywords

Greenhouse Gases; Temperature; Environmental Protection; Machine Learning; Regression

Abstract

Since the 21st century, the industry has developed rapidly, and emissions of various greenhouse gases have reached new highs. Global warming has also led to more natural disasters. Choosing a suitable model to analyze the influence of several gas concentrations in the atmosphere on the overall temperature change since the 21st century can predict climate trends to a certain extent. This article compares the MAE, RMSE, and R-Square of the machine learning models of Linear Regression, ElasticNet Regression, Random Forest, Extra Trees, SVR, Gradient Boosting Regression Tree, XGBoost to predict temperature changes. Finally, as the atmospheric concentrations of the four greenhouse gases rise, the temperature deviation from 1951 - 1980 mean also gradually increased may even be exceeding 1.5°C in the future. It is found that among these methods, Gradient Boosted Tree has better results than R-square of it comes to 0.72 which is the largest one and a relatively good value, so it can be used as a model for predicting temperature changes.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - Zixian Gong
PY  - 2022
DA  - 2022/04/29
TI  - The World Temperature Changes Related to Atmospheric Concentrations in the 21st Century Based on Machine Learning
BT  - Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022)
PB  - Atlantis Press
SP  - 311
EP  - 315
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220405.051
DO  - 10.2991/aebmr.k.220405.051
ID  - Gong2022
ER  -