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A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

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GNSS Research Center, Wuhan University, Wuhan 430079, China
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School of Civil Engineering, Wuhan University, Wuhan 430072, China
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School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
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Huaibei Administration for Market Regulation, Huaibei 235000, China
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Authors to whom correspondence should be addressed.
Symmetry 2020, 12(1), 139; https://doi.org/10.3390/sym12010139 (registering DOI)
Received: 8 December 2019 / Revised: 5 January 2020 / Accepted: 7 January 2020 / Published: 9 January 2020
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. View Full-Text
Keywords: RBF neural network; Laplacian feature map; climate change; extreme weather; support vector machine RBF neural network; Laplacian feature map; climate change; extreme weather; support vector machine
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Ren, X.; Li, L.; Yu, Y.; Xiong, Z.; Yang, S.; Du, W.; Ren, M. A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method. Symmetry 2020, 12, 139.

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