Causal analysis of global warming: based on ARIMA and LSTM models

Xinyi Liu; Yuwei Wu; Yijie Gao

doi:10.1117/12.3011952

7 December 2023 Causal analysis of global warming: based on ARIMA and LSTM models

Xinyi Liu, Yuwei Wu, Yijie Gao

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129412A (2023) https://doi.org/10.1117/12.3011952
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Global warming, which is deeply related with our life, is becoming more serious overtime. Few scholars have used deep learning models to predict changes in temperature. Most of the existing studies are single temperature predictions and no mechanistic analysis has been carried out. Therefore, we have designed two models to predict the future temperature: ARIMA model and LSTM model. With these two models, we have provided a scientific prediction of future temperature increase. In addition, we also performed a correlation analysis to look for the factors influencing temperature. Considering all models together, we agree that global temperatures will reach 20°C by 2050, and we find that anthropogenic activities have a greater impact on global warming.

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

Citation Download Citation

Xinyi Liu, Yuwei Wu, and Yijie Gao "Causal analysis of global warming: based on ARIMA and LSTM models", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129412A (7 December 2023); https://doi.org/10.1117/12.3011952

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