A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

Autores
Wang, Yakun; Shi, Liangsheng; Lin, Lin; Holzman, Mauro Ezequiel; Carmona, Facundo; Zhang, Qiuru
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
As the collection of soil moisture data is often costly, it is essential to implement data-worth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-to-obtain meteorological data into GP training yielded better data-worth assessment.
Fil: Wang, Yakun. Wuhan University; China
Fil: Shi, Liangsheng. Wuhan University; China
Fil: Lin, Lin. Wuhan University; China
Fil: Holzman, Mauro Ezequiel. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; Argentina
Fil: Carmona, Facundo. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; Argentina
Fil: Zhang, Qiuru. Wuhan University; China
Materia
soil moisture
machine learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/163420
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learningWang, YakunShi, LiangshengLin, LinHolzman, Mauro EzequielCarmona, FacundoZhang, Qiurusoil moisturemachine learninghttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1As the collection of soil moisture data is often costly, it is essential to implement data-worth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-to-obtain meteorological data into GP training yielded better data-worth assessment.Fil: Wang, Yakun. Wuhan University; ChinaFil: Shi, Liangsheng. Wuhan University; ChinaFil: Lin, Lin. Wuhan University; ChinaFil: Holzman, Mauro Ezequiel. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; ArgentinaFil: Carmona, Facundo. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; ArgentinaFil: Zhang, Qiuru. Wuhan University; ChinaSoil Science Society of America2020-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/163420Wang, Yakun; Shi, Liangsheng; Lin, Lin; Holzman, Mauro Ezequiel; Carmona, Facundo; et al.; A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning; Soil Science Society of America; Vadose Zone Journal; 19; 1; 5-2020; 1-181539-1663CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/vzj2.20026info:eu-repo/semantics/altIdentifier/doi/10.1002/vzj2.20026info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:43:50Zoai:ri.conicet.gov.ar:11336/163420instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:43:50.327CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
title A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
spellingShingle A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
Wang, Yakun
soil moisture
machine learning
title_short A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
title_full A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
title_fullStr A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
title_full_unstemmed A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
title_sort A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning
dc.creator.none.fl_str_mv Wang, Yakun
Shi, Liangsheng
Lin, Lin
Holzman, Mauro Ezequiel
Carmona, Facundo
Zhang, Qiuru
author Wang, Yakun
author_facet Wang, Yakun
Shi, Liangsheng
Lin, Lin
Holzman, Mauro Ezequiel
Carmona, Facundo
Zhang, Qiuru
author_role author
author2 Shi, Liangsheng
Lin, Lin
Holzman, Mauro Ezequiel
Carmona, Facundo
Zhang, Qiuru
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv soil moisture
machine learning
topic soil moisture
machine learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv As the collection of soil moisture data is often costly, it is essential to implement data-worth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-to-obtain meteorological data into GP training yielded better data-worth assessment.
Fil: Wang, Yakun. Wuhan University; China
Fil: Shi, Liangsheng. Wuhan University; China
Fil: Lin, Lin. Wuhan University; China
Fil: Holzman, Mauro Ezequiel. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; Argentina
Fil: Carmona, Facundo. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; Argentina
Fil: Zhang, Qiuru. Wuhan University; China
description As the collection of soil moisture data is often costly, it is essential to implement data-worth analysis in advance to obtain a cost-effective data collection scheme. In previous data-worth analysis, the model structural error is often neglected. In this paper, we propose a robust data-worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data-worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data-worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data-worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy-to-obtain meteorological data into GP training yielded better data-worth assessment.
publishDate 2020
dc.date.none.fl_str_mv 2020-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/163420
Wang, Yakun; Shi, Liangsheng; Lin, Lin; Holzman, Mauro Ezequiel; Carmona, Facundo; et al.; A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning; Soil Science Society of America; Vadose Zone Journal; 19; 1; 5-2020; 1-18
1539-1663
CONICET Digital
CONICET
url http://hdl.handle.net/11336/163420
identifier_str_mv Wang, Yakun; Shi, Liangsheng; Lin, Lin; Holzman, Mauro Ezequiel; Carmona, Facundo; et al.; A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning; Soil Science Society of America; Vadose Zone Journal; 19; 1; 5-2020; 1-18
1539-1663
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/vzj2.20026
info:eu-repo/semantics/altIdentifier/doi/10.1002/vzj2.20026
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Soil Science Society of America
publisher.none.fl_str_mv Soil Science Society of America
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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