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dc.contributor.authorKolevatova, Anastasiia
dc.contributor.authorRiegler, Michael
dc.contributor.authorCherubini, Francesco
dc.contributor.authorHu, Xiangping
dc.contributor.authorHammer, Hugo Lewi
dc.date.accessioned2022-02-04T15:20:27Z
dc.date.available2022-02-04T15:20:27Z
dc.date.created2021-10-18T20:58:52Z
dc.date.issued2021-10-15
dc.identifier.citationBig Data and Cognitive Computing. 2021, 5 (4), .en_US
dc.identifier.issn2504-2289
dc.identifier.urihttps://hdl.handle.net/11250/2977266
dc.description.abstractA general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that random forest (RF) outperformed other ML methods, and especially linear regression models representing current practice in the literature. Explainable artificial intelligence (XAI) was further used to interpret the RF method and analyze the impact of different LC changes on temperature. The results mainly agree with the climate science literature, but also reveal new and interesting findings, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation, and XAI.en_US
dc.description.sponsorshipFrancesco Cherubini and Xiangping Hu received support of the Norwegian Research Council through the project MitiStress (project no. 286773).en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofseriesBig Data and Cognitive Computing;Volume 5, Issue 4
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectClimate scienceen_US
dc.subjectExplainable artificial intelligenceen_US
dc.subjectLand cover changesen_US
dc.subjectMachine learningen_US
dc.subjectUncertainty quantificationen_US
dc.titleUnraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligenceen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2021 by the authorsen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.3390/bdcc5040055
dc.identifier.cristin1946861
dc.source.journalBig Data and Cognitive Computingen_US
dc.source.volume5en_US
dc.source.issue4en_US
dc.source.pagenumber1-17en_US
dc.relation.projectNorges forskningsråd: 286773en_US


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