A simplified climate change model and extreme weather model based on a machine learning method

Xiaobin Ren, Lianyan Li, Yang Yu, Zhihua Xiong, Shunzhou Yang, Wei Du, Mengjia Ren

Research output: Contribution to journalArticlepeer-review

Abstract

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.
Original languageEnglish
Article number139
Number of pages29
JournalSymmetry
Volume12
Issue number1
DOIs
Publication statusPublished - 2020

Open Access - Access Right Statement

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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