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

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.

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“…By applying modern mathematical models, some studies have proposed methods for accurately predicting climate change effects. [24][25][26][27] The optimization Maxent model presented in Li et al, 24 which is based on the method of maximum entropy, is a useful example of kind distribution. However, the range of appropriate variables within an environment, along with type parameters, is an essential factor that must be considered when applying this model.…”

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