Regularization Learning of Trace Element Contamination Stemmed from Tailings Dam-Break

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dc.contributor.authorTutmez, Bulent
dc.contributor.authorKomori, Osamu
dc.date.accessioned2024-08-04T20:54:43Z
dc.date.available2024-08-04T20:54:43Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAn important practice in environmental risk management is assessing the consequences of heavy metal concentrations resulting from a mine dam tailing failure on soil, water, and trees. To ap-praise the extent of pollution, an effective classification is essential. In this study, trace element contamination is handled as a two-group classification problem and examined the performance of supervised regularization algorithms as spatial classifiers using imbalanced uncertain data. In addition to conventional shrinkage algorithms such as Ridge, the Lasso and Elastic-Net, the generalized t-statistic-based U-Lasso classifiers have been introduced and tested for mitigating such imbalances and adjusting weights for class distributions. The feature interpretation studies underlined that the most important indicator of the models is Zinc (Zn). The experimental stud-ies revealed that the Ridge classifier (l2penalty) outperforms the other models. Statistically, the U-Lasso models exhibited notable explanation capacity and their performances recorded close to the conventional shrinkage algorithms. The use of statistical learning-based classification ap-proach to appraise geo-environmental contamination under the conditions of natural variability and spatial uncertainty provides useful meta-data and reliable classification models.en_US
dc.description.sponsorshipGrants-in-Aid for Scientific Research [21H04874] Funding Source: KAKENen_US
dc.identifier.doi10.22059/poll.2023.349809.1657
dc.identifier.endpage1097en_US
dc.identifier.issn2383-451X
dc.identifier.issn2383-4501
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85172698930en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1082en_US
dc.identifier.urihttps://doi.org/10.22059/poll.2023.349809.1657
dc.identifier.urihttps://hdl.handle.net/11616/101595
dc.identifier.volume9en_US
dc.identifier.wosWOS:001015509000018en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUniv Tehranen_US
dc.relation.ispartofPollutionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectContaminationen_US
dc.subjectTailings Dam Failureen_US
dc.subjectRegularizationen_US
dc.subjectU-Lassoen_US
dc.titleRegularization Learning of Trace Element Contamination Stemmed from Tailings Dam-Breaken_US
dc.typeArticleen_US

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