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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 649 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 633 | - |
dc.citation.title | GISCIENCE & REMOTE SENSING | - |
dc.citation.volume | 57 | - |
dc.contributor.author | Cho, Dongjin | - |
dc.contributor.author | Yoo, Cheolhee | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Lee, Yeonsu | - |
dc.contributor.author | Lee, Jaese | - |
dc.date.accessioned | 2023-12-21T17:16:13Z | - |
dc.date.available | 2023-12-21T17:16:13Z | - |
dc.date.created | 2020-06-18 | - |
dc.date.issued | 2020-07 | - |
dc.description.abstract | The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (T-max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T-max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields. | - |
dc.identifier.bibliographicCitation | GISCIENCE & REMOTE SENSING, v.57, no.5, pp.633 - 649 | - |
dc.identifier.doi | 10.1080/15481603.2020.1766768 | - |
dc.identifier.issn | 1548-1603 | - |
dc.identifier.scopusid | 2-s2.0-85085500568 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/32358 | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/15481603.2020.1766768 | - |
dc.identifier.wosid | 000535114500001 | - |
dc.language | 영어 | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Geography, Physical; Remote Sensing | - |
dc.relation.journalResearchArea | Physical Geography; Remote Sensing | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Spatial interpolation | - |
dc.subject.keywordAuthor | Cokriging | - |
dc.subject.keywordAuthor | Multi-linear regression | - |
dc.subject.keywordAuthor | Support vector regression | - |
dc.subject.keywordAuthor | Random forest | - |
dc.subject.keywordAuthor | Simple average ensemble | - |
dc.subject.keywordAuthor | Stacking ensemble | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORKS | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINE | - |
dc.subject.keywordPlus | SURFACE-TEMPERATURE | - |
dc.subject.keywordPlus | TERM PREDICTION | - |
dc.subject.keywordPlus | RANDOM FOREST | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | MORTALITY | - |
dc.subject.keywordPlus | WEATHER | - |
dc.subject.keywordPlus | MODEL | - |
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