Mimar Sinan Güzel Sanatlar Üniversitesi Açık Bilim, Sanat Arşivi
Açık Bilim, Sanat Arşivi, Mimar Sinan Güzel Sanatlar Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.MSGSÜ'de Ara
Functional regression on remote sensing data in oceanography
dc.contributor.author | Acar-Denizli, Nihan | |
dc.contributor.author | Delicado, Pedro | |
dc.contributor.author | Basarir, Gulay | |
dc.contributor.author | Caballero, Isabel | |
dc.date.accessioned | 2025-01-09T20:14:24Z | |
dc.date.available | 2025-01-09T20:14:24Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 1352-8505 | |
dc.identifier.issn | 1573-3009 | |
dc.identifier.uri | https://doi.org/10.1007/s10651-018-0405-7 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14124/9044 | |
dc.description.abstract | The aim of this study is to propose the use of a functional data analysis approach as an alternative to the classical statistical methods most commonly used in oceanography and water quality management. In particular we consider the prediction of total suspended solids (TSS) based on remote sensing (RS) data. For this purpose several functional linear regression models and classical non-functional regression models are applied to 10 years of RS data obtained from medium resolution imaging spectrometer sensor to predict the TSS concentration in the coastal zone of the Guadalquivir estuary. The results of functional and classical approaches are compared in terms of their mean square prediction error values and the superiority of the functional models is established. A simulation study has been designed in order to support these findings and to determine the best prediction model for the TSS parameter in more general contexts. | en_US |
dc.description.sponsorship | Spanish Ministerio de Ciencia e Innovacion; Fondo Europeo de Desarrollo Regional Grants [MTM2013-43992-R, MTM2017-88142-P]; Mimar Sinan Fine Arts University Coordinatorship of Scientific Research Projects [2014-24] | en_US |
dc.description.sponsorship | This investigation is partially supported by the Spanish Ministerio de Ciencia e Innovacion and Fondo Europeo de Desarrollo Regional Grants MTM2013-43992-R and MTM2017-88142-P, and by the Project 2014-24 of Mimar Sinan Fine Arts University Coordinatorship of Scientific Research Projects. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Environmental and Ecological Statistics | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Exponential regression models | en_US |
dc.subject | Functional linear regression models | en_US |
dc.subject | Functional partial least squares | en_US |
dc.subject | Functional principal components | en_US |
dc.subject | Remote sensing data | en_US |
dc.title | Functional regression on remote sensing data in oceanography | en_US |
dc.type | article | en_US |
dc.authorid | Acar-Denizli, Nihan/0000-0002-0012-8632 | |
dc.authorid | Caballero, Isabel/0000-0001-7485-0989 | |
dc.authorid | Delicado, Pedro/0000-0003-3933-4852 | |
dc.department | Mimar Sinan Güzel Sanatlar Üniversitesi | en_US |
dc.identifier.doi | 10.1007/s10651-018-0405-7 | |
dc.identifier.volume | 25 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.startpage | 277 | en_US |
dc.identifier.endpage | 304 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.wosquality | Q3 | |
dc.identifier.wos | WOS:000432320500006 | |
dc.identifier.scopus | 2-s2.0-85047200429 | |
dc.identifier.scopusquality | Q1 | |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.snmz | KA_20250105 |
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