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

Zhenning Li

doi:10.1117/12.3003793

11 October 2023 Exploration of global temperature warming based on deep learning and statistical analysis

Zhenning Li

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003P (2023) https://doi.org/10.1117/12.3003793
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenning Li "Exploration of global temperature warming based on deep learning and statistical analysis", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003P (11 October 2023); https://doi.org/10.1117/12.3003793

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