AI for sustainable land management and greenhouse gas emission forecasting: advancing climate action
Atmospheric methane, a powerful greenhouse gas (GHG), plays a significant role in accelerating global warming. This study leverages machine learning to analyze methane emissions and forecast their patterns using comprehensive datasets, including TROPOMI Sentinel-5P satellite methane data, MIDAS UK soil temperature records, and the NERC EDS 2021 land cover dataset. By constructing predictive models through a machine learning pipeline, this research identifies key drivers of methane emissions and addresses data gaps caused by incomplete satellite measurements. Experimental results demonstrate the superior performance of the Random Forest model compared to other machine learning models, achieving the highest accuracy with an RMSE of 29.49 and an MAE of 20.84 for methane (CH4) prediction. Shapley analysis is used to enhance model explainability by evaluating how different attributes (e.g., time, land usage, soil temperature) influence methane production. This study highlights a notable increase in methane levels over recent years, and underscores the critical role of sustainable land management, particularly agricultural practices, in strategies to reduce methane emissions and combat global warming. This work is part of a broader initiative to develop data-driven, AI-powered Digital Twins for analyzing the interplay between human activities and natural processes in advancing climate action.
Funding
Self-Learning Digital Twins for Sustainable Land Management
Engineering and Physical Sciences Research Council
Find out more...History
School
- Science
Department
- Computer Science
Published in
Proceedings 2025 Asia Conference on Algorithms, Computing and Machine Learning (CACML)Source
Conference on Algorithms, Computing and Machine Learning (CACML 2025)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
This accepted manuscript has been made available under the Creative Commons Attribution licence (CC BY) under the IEEE JISC UK green open access agreement.Acceptance date
2025-02-04Publication date
2025-03-28Copyright date
2025Notes
The conference website is available here: https://www.cacml.net/ISBN
9798331543143Publisher version
Language
- en