Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/234418 |
Resumo: | This work presents an uncertainty analysis applied to the results of an ecological model. This model describes the development of submerged macrophytes colonization in a brazilian reservoir, between Sao Paulo and Parana states. To build the model we map the submerged vegetation with hydroacoustic technique to estimate submerged canopy height. Data about the light penetration into the water were also collected in some points. The dynamic model was elaborated with two variables: depth and attenuation coefficient (kt). Monte Carlo technique was used to evaluate how the existing uncertainty in the data acquisition process and measurement tools, propagated to the kriging interpolation, affects the model results. It was possible to evaluate the model output histograms, and the Root Mean Square Error (RMSE) of each simulated point in relation to the observed one. The confidence intervals were also calculated with the 5th and 95th percentiles. With this uncertainty analysis, the interval time and the points with the lowest uncertainty could be identified. |
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Repositório Institucional da UNESP |
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Uncertainty analysis of a spatiotemporal model for submerged vegetation colonizationEcologyKrigingMacrophytesMappingMonte CarloThis work presents an uncertainty analysis applied to the results of an ecological model. This model describes the development of submerged macrophytes colonization in a brazilian reservoir, between Sao Paulo and Parana states. To build the model we map the submerged vegetation with hydroacoustic technique to estimate submerged canopy height. Data about the light penetration into the water were also collected in some points. The dynamic model was elaborated with two variables: depth and attenuation coefficient (kt). Monte Carlo technique was used to evaluate how the existing uncertainty in the data acquisition process and measurement tools, propagated to the kriging interpolation, affects the model results. It was possible to evaluate the model output histograms, and the Root Mean Square Error (RMSE) of each simulated point in relation to the observed one. The confidence intervals were also calculated with the 5th and 95th percentiles. With this uncertainty analysis, the interval time and the points with the lowest uncertainty could be identified.Federal University of Technology - Parana (UTFPR)Sao Paulo State UniversitySao Paulo State UniversityFederal University of Technology - Parana (UTFPR)Universidade Estadual Paulista (UNESP)Batista, Ligia Flávia Antunes [UNESP]Imai, Nilton Nobuhiro [UNESP]Da Silva Rotta, Luiz Henrique [UNESP]Watanabe, Fernanda Sayuri Yoshino [UNESP]Velini, Edivaldo Domingues [UNESP]2022-05-02T14:58:24Z2022-05-02T14:58:24Z2012-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article37-42Accuracy 2012 - Proceedings of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, p. 37-42.http://hdl.handle.net/11449/2344182-s2.0-84975730919Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAccuracy 2012 - Proceedings of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciencesinfo:eu-repo/semantics/openAccess2024-04-30T15:56:16Zoai:repositorio.unesp.br:11449/234418Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-30T15:56:16Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
title |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
spellingShingle |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization Batista, Ligia Flávia Antunes [UNESP] Ecology Kriging Macrophytes Mapping Monte Carlo |
title_short |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
title_full |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
title_fullStr |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
title_full_unstemmed |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
title_sort |
Uncertainty analysis of a spatiotemporal model for submerged vegetation colonization |
author |
Batista, Ligia Flávia Antunes [UNESP] |
author_facet |
Batista, Ligia Flávia Antunes [UNESP] Imai, Nilton Nobuhiro [UNESP] Da Silva Rotta, Luiz Henrique [UNESP] Watanabe, Fernanda Sayuri Yoshino [UNESP] Velini, Edivaldo Domingues [UNESP] |
author_role |
author |
author2 |
Imai, Nilton Nobuhiro [UNESP] Da Silva Rotta, Luiz Henrique [UNESP] Watanabe, Fernanda Sayuri Yoshino [UNESP] Velini, Edivaldo Domingues [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Federal University of Technology - Parana (UTFPR) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Batista, Ligia Flávia Antunes [UNESP] Imai, Nilton Nobuhiro [UNESP] Da Silva Rotta, Luiz Henrique [UNESP] Watanabe, Fernanda Sayuri Yoshino [UNESP] Velini, Edivaldo Domingues [UNESP] |
dc.subject.por.fl_str_mv |
Ecology Kriging Macrophytes Mapping Monte Carlo |
topic |
Ecology Kriging Macrophytes Mapping Monte Carlo |
description |
This work presents an uncertainty analysis applied to the results of an ecological model. This model describes the development of submerged macrophytes colonization in a brazilian reservoir, between Sao Paulo and Parana states. To build the model we map the submerged vegetation with hydroacoustic technique to estimate submerged canopy height. Data about the light penetration into the water were also collected in some points. The dynamic model was elaborated with two variables: depth and attenuation coefficient (kt). Monte Carlo technique was used to evaluate how the existing uncertainty in the data acquisition process and measurement tools, propagated to the kriging interpolation, affects the model results. It was possible to evaluate the model output histograms, and the Root Mean Square Error (RMSE) of each simulated point in relation to the observed one. The confidence intervals were also calculated with the 5th and 95th percentiles. With this uncertainty analysis, the interval time and the points with the lowest uncertainty could be identified. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-01-01 2022-05-02T14:58:24Z 2022-05-02T14:58:24Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Accuracy 2012 - Proceedings of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, p. 37-42. http://hdl.handle.net/11449/234418 2-s2.0-84975730919 |
identifier_str_mv |
Accuracy 2012 - Proceedings of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, p. 37-42. 2-s2.0-84975730919 |
url |
http://hdl.handle.net/11449/234418 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Accuracy 2012 - Proceedings of the 10th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
37-42 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803046358995697664 |