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Simulating Temperature in a Soil Incubation Experiment
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Forecasting and analysing global average temperature trends based on LSTM and ARIMA models.

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Global warming trends are significant, but COVID-19 temporarily slowed greenhouse gas emissions. This study combines LSTM and ARIMA models for accurate global temperature forecasting, aiding environmental policy.

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Area of Science:

  • Climate Science
  • Time Series Analysis
  • Environmental Science

Background:

  • Established correlation between global average temperature and greenhouse gases.
  • Limited exploration of combined LSTM and ARIMA models for enhanced temperature prediction accuracy.
  • Hypothesis on COVID-19's impact on global average temperature.

Purpose of the Study:

  • To investigate the combined potential of LSTM and ARIMA models for global temperature forecasting.
  • To assess the influence of COVID-19 on global average temperature trends.
  • To provide a more accurate and comprehensive temperature prediction method.

Main Methods:

  • Utilized global average temperature data spanning from 1880 to 2022.
  • Combined Long Short-Term Memory (LSTM) model for long-term dependencies with Autoregressive Integrated Moving Average (ARIMA) model for linear time series data.
  • Employed a hybrid model to leverage the strengths of both LSTM and ARIMA.

Main Results:

  • Confirmed a significant early trend of global temperature rise, with implementation delays causing issues.
  • Found that COVID-19 indirectly reduced greenhouse gas emissions, mitigating global warming.
  • Observed a weak correlation between longitude and mean temperature, and a strong negative correlation between latitude and temperature.

Conclusions:

  • The hybrid LSTM-ARIMA model offers a more accurate and comprehensive approach to global temperature forecasting.
  • COVID-19's impact on emissions suggests a temporary slowdown in global warming.
  • Findings provide critical insights for ecological governance and economic policy formulation.