A machine learning approach for resource mapping analysis of greenhouse gas removal technologies

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dc.contributor.authorAsibor, Jude Odianosen
dc.contributor.authorClough, Peter T.
dc.contributor.authorNabavi, Seyed Ali
dc.contributor.authorManovic, Vasilije
dc.date.accessioned2023-08-08T14:08:04Z
dc.date.available2023-08-08T14:08:04Z
dc.date.issued2023-07-25
dc.description.abstractIn this study, machine learning (ML) was applied to investigate the suitability of a location to deploy five greenhouse gas removal (GGR) methods within a global context, based on a location's bio-geophysical and techno-economic characteristics. The GGR methods considered are forestation, enhanced weathering (EW), direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS) and biochar. An unsupervised ML (hierarchical clustering) technique was applied to label the dataset. Seven supervised ML algorithms were applied in training and testing the labelled dataset with the k-Nearest neighbour (k-NN), Artificial Neural Network (ANN) and Random Forest algorithms having the highest performance accuracies of 96%, 98% and 100% respectively. A case study of Scotland's suitability to deploy these GGR methods was carried out with obtained results indicating a high correlation between the ML model results and information in the available literature. While the performance accuracy of the ML models was typically high (76 - 100%), an assessment of its decision-making logic (model interpretation) revealed some limitations regarding the impact of the various input variables on the outputs.en_UK
dc.identifier.citationAsibor JO, Clough PT, Nabavi SA, Manovic V. (2023) A machine learning approach for resource mapping analysis of greenhouse gas removal technologies, Energy and Climate Change, Volume 4, December 2023, Article Number 100112en_UK
dc.identifier.issn2666-2787
dc.identifier.urihttps://doi.org/10.1016/j.egycc.2023.100112
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20068
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine learningen_UK
dc.subjectClimate change mitigationen_UK
dc.subjectCarbon capture and storageen_UK
dc.subjectNegative emission technologiesen_UK
dc.subjectRandom foresten_UK
dc.subjectBECCSen_UK
dc.titleA machine learning approach for resource mapping analysis of greenhouse gas removal technologiesen_UK
dc.typeArticleen_UK

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