Exploration of global temperature warming based on deep learning and statistical analysis

Global temperature change threatens our planet's ecosystems. This study introduces a new approach to address the problem of global temperature change. A global temperature prediction model was developed using LSTM and ARIMA techniques. The Pearson correlation model was used to investigate the relationship between temperature change and related factors, while an independent sample t-test model determined whether forest fires significantly affect temperature. The method has practical implications in climate monitoring and management, and high accuracy was achieved in the experiment. The findings provide valuable insights into the dynamics of global temperature change and lay the foundation for policymakers to implement evidence-based interventions to reduce the impacts of climate change on the Earth's ecosystems.