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Updated: Jun 7, 2025
06:10
Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
890
Global forecasting of carbon concentration through a deep learning spatiotemporal modeling
Marc Semper1, Manuel Curado1, Jose F Vicent1
1Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente del Raspeig, Ap. Correos 99, E-03080, Alicante, Spain.
Journal of Environmental Management
|November 14, 2024
Summary
Accurate carbon concentration predictions are crucial for climate change mitigation policies. Deep learning models, especially graphical neural networks using satellite data, show high accuracy in forecasting carbon dioxide and methane levels globally.
Area of Science:
- Environmental Science
- Data Science
- Climate Science
Background:
- Effective climate change mitigation requires accurate carbon concentration reduction policies.
- Predicting carbon concentration trends faces challenges due to spatiotemporal correlations and diverse influencing factors.
Purpose of the Study:
- To propose and evaluate deep learning strategies for predicting global carbon dioxide and methane concentrations.
- To enhance predictive accuracy by integrating satellite observations and complementary environmental variables.
Main Methods:
- Utilized deep learning techniques, including graphical neural networks.
- Employed satellite observations for six-month global projections.
- Incorporated diverse environmental variables (dynamic and static) into the models.
Main Results:
- Demonstrated high accuracy in predicting carbon dioxide and methane concentrations.
- Graphical neural network-based models showed particular effectiveness.
- Validated the capability of deep learning to integrate dynamic and static information for accurate predictions.
Conclusions:
- Deep learning techniques hold significant potential for accurate carbon concentration forecasting.
- Graphical neural networks are a promising approach for predicting greenhouse gas emissions.
- Accurate prediction models are vital for developing effective climate change mitigation strategies.
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