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dc.contributor.authorYuan, Fuqing
dc.contributor.authorLu, Jinmei
dc.date.accessioned2019-03-05T12:29:22Z
dc.date.available2019-03-05T12:29:22Z
dc.date.issued2018
dc.description.abstractEnvironmental data exhibits as huge amount and complex dependency. Utilizing these data to detect anomaly is beneficial to the disaster prevention. Big data approach using the machine learning method has the advantage not requiring the geophysical and geochemical knowledge to detect anomaly. This paper using the popular support vector regression (SVR ) to model the correlation between factors. From the residual of the regression, it develops a statistical method to quantify the extremity of some abnormal observed data. A case study is proposed to demonstrate the developed methods.en_US
dc.description.sponsorshipWe appreciate the support from the EU Interreg Min-north project. The project provides the original idea of the method and the finance for the research.en_US
dc.descriptionPublished version, licensed under the terms of the <a href=https://creativecommons.org/licenses/by/3.0/>Creative Commons Attribution 3.0 </a>. Source at <a href=https://doi.org/10.1088/1757-899X/472/1/012089>https://doi.org/10.1088/1757-899X/472/1/012089</a>en_US
dc.identifier.citationYuan F, Lu J. (2018) Anomaly Detection for Environmental Data Using Machine Learning Regression. <i>IOP Conference Series: Materials Science and Engineering 472 </i> (1), 5 s. https://doi.org/10.1088/1757-899X/472/1/012089en_US
dc.identifier.cristinIDFRIDAID 1636832
dc.identifier.doi10.1088/1757-899X/472/1/012089
dc.identifier.issn1757-8981
dc.identifier.issn1757-899X
dc.identifier.urihttps://hdl.handle.net/10037/14849
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.relation.journalIOP Conference Series: Materials Science and Engineering
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleAnomaly Detection for Environmental Data Using Machine Learning Regressionen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US


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