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AI for sustainable land management and greenhouse gas emission forecasting: advancing climate action

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posted on 2025-02-11, 10:10 authored by Jack Cutler, Baihua LiBaihua Li, Bashar Alhnaity, Tom Partridge, Mike Thompson, Qinggang MengQinggang Meng

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

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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

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher 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

2025-02-04

Publication date

2025-03-28

Copyright date

2025

Notes

The conference website is available here: https://www.cacml.net/

ISBN

9798331543143

Language

  • en

Location

Guangzhou, China

Event dates

28th March 2025 - 30th March 2025

Depositor

Prof Baihua Li. Deposit date: 10 February 2025

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