Global Warming OR Not?

Authors

  • Haoyang Tu

DOI:

https://doi.org/10.56028/fesr.2.4.1.2024

Keywords:

Global Warming, LSTM, ARIMA, The Gray Correlation Analysis, The Multiple Linear Regression Model, the Pearson correlation, Pettitt Mutation Detection.

Abstract

Recent years have seen rising Earth's temperatures and a shrinking Antarctic land area. Studying factors affecting global warming and finding solutions to slow it is crucial. Using the Pettitt mutation point detection method, we examined March temperatures over the past decade and identified a significant change in March 2015. Predictive models (ARIMA and LSTM) forecast similar future temperatures, with the LSTM model showing slightly higher accuracy. Gray correlation and multiple linear regression analyses identified CO2 as the primary factor influencing global temperatures. Reducing CO2 emissions is essential to curb global warming.

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Published

2024-10-16