Machine learning-based modelling of zenith wet delay using terrestrial meteorological data in the Brazilian territory

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCzech University of Life Sciences Prague-
Autor(es): dc.creatorAlbuquerque, Afonso Marques-
Autor(es): dc.creatorNespolo, Raphael Silva-
Autor(es): dc.creatorTommaselli, Antonio Maria Garcia-
Autor(es): dc.creatorMartins-Neto, Rorai Perreira-
Autor(es): dc.creatorImai, Nilton Nobuhiro-
Autor(es): dc.creatorAlves, Daniele Barroca Marra-
Autor(es): dc.creatorGouveia, Tayna Aparecida Ferreira-
Autor(es): dc.creatorJerez, Gabriel Oliveira-
Data de aceite: dc.date.accessioned2025-08-21T15:25:28Z-
Data de disponibilização: dc.date.available2025-08-21T15:25:28Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-03-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5194/isprs-annals-X-3-2024-13-2024-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/299734-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/299734-
Descrição: dc.descriptionThe Zenith Total Delay (ZTD) is one of the primary error sources derived from the neutral atmosphere associated with the GNSS (Global Navigation Satellite Systems) technique. Zenith Wet Delay (ZWD) is the smallest part of the ZTD, but the high variability is caused by spatial-temporal variation, making the modelling of this component a challenging task. Although ZWD is considered an error in GNSS positioning, it is also a variable composed mainly of water vapour and can, therefore, be used for atmospheric investigations, and assists in climate studies for precipitation events. In this work, a model was trained to estimate the delay wet component from surface atmospheric parameters. The training data comes from 29 radiosonde stations around Brazil, for a six-year period (2017 to 2022), with data collected at 12 h UTC (Universal Time Coordinated). The model was validated using the holdout technique, with 70% of the data used in training and 30% for validation (cross-validation analysis). The generated model achieved a RMSE (Root Mean Squared Error) of approximately 38 mm, with an 81% of determination coefficient.-
Descrição: dc.descriptionČeská Zemědělská Univerzita v Praze-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFaculty of Science and Technology São Paulo State University-
Descrição: dc.descriptionFaculty of Forestry and Wood Sciences Czech University of Life Sciences Prague-
Descrição: dc.descriptionFaculty of Science and Technology São Paulo State University-
Descrição: dc.descriptionCNPq: 116545/2023-2-
Descrição: dc.descriptionCNPq: 151351/2019-8-
Descrição: dc.descriptionFAPESP: 2021/05285-0-
Descrição: dc.descriptionFAPESP: 2021/06029-7-
Descrição: dc.descriptionFAPESP: 2023/14739-0-
Descrição: dc.descriptionCNPq: 303670/2018-5-
Descrição: dc.descriptionCNPq: 306112/2023-0-
Descrição: dc.descriptionCNPq: 308747/2021-6-
Descrição: dc.descriptionCAPES: 88887.310313/2018-00-
Descrição: dc.descriptionCAPES: 88887.898553/2023-00-
Descrição: dc.descriptionCAPES: 88887.961778/2024-00-
Formato: dc.format13-19-
Idioma: dc.languageen-
Relação: dc.relationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectMeteorological stations-
Palavras-chave: dc.subjectRandom Forest-
Palavras-chave: dc.subjectZenith Wet Delay (ZWD)-
Título: dc.titleMachine learning-based modelling of zenith wet delay using terrestrial meteorological data in the Brazilian territory-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.