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Advanced utilization of multi-learning algorithm: ensemble super learner to map groundwater potential for potable mineral water

DC Field Value Language
dc.contributor.authorLee, Sanghoon-
dc.contributor.authorKaown, Dugin-
dc.contributor.authorKoh, Eun-Hee-
dc.contributor.authorLee, Hye-Lim-
dc.contributor.authorKo, Kyung-Seok-
dc.contributor.authorLee, Kang-Kun-
dc.date.accessioned2022-06-24T08:29:08Z-
dc.date.available2022-06-24T08:29:08Z-
dc.date.created2022-05-19-
dc.date.issued2022-01-
dc.identifier.citationGeocarto International-
dc.identifier.issn1010-6049-
dc.identifier.urihttps://hdl.handle.net/10371/184107-
dc.description.abstract© 2022 Informa UK Limited, trading as Taylor & Francis Group.Although mapping the groundwater quality is crucial for people who require groundwater with strict quality standards, the ability to take intensive measurements has been restricted by a lack of groundwater accessibility. Thus, this study aimed to estimate and map the suitability of groundwater quality for use as potable mineral water. We attempted a novel approach by targeting comprehensive qualities for a specific groundwater use and by adopting a super learner that combines multiple different learning algorithms. The super learner generated a groundwater potential map indicating a zone with a high potential for mineral water and it outperformed the base learners by 21%–74%. Estimation results designated appropriate groundwater development locations for mineral water use, and assessment of predictors determined favorable environments. Consequently, the proposed approach presented a possible method for finding groundwater with the required quality for its optimal usage. Furthermore, it provided the possibility of worldwide application.-
dc.language영어-
dc.publisherGeocarto International Centre-
dc.titleAdvanced utilization of multi-learning algorithm: ensemble super learner to map groundwater potential for potable mineral water-
dc.typeArticle-
dc.identifier.doi10.1080/10106049.2022.2025921-
dc.citation.journaltitleGeocarto International-
dc.identifier.wosid000740912300001-
dc.identifier.scopusid2-s2.0-85122760538-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorLee, Kang-Kun-
dc.type.docTypeArticle-
dc.description.journalClass1-
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