Forecasting 7BE concentrations in surface air using time series analysis

[EN] 7Be is a cosmogenic radionuclide widely used as an atmospheric tracer, whose evaluation and forecasting can provide valuable information on changes in the atmospheric behavior. In this study, measurements of 7Be concentrations were made each month during the period 2007-2015 from samples of atm...

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Autores: Bas Cerdá, María del Carmen, Ortiz Moragón, Josefina, Ballesteros Pascual, Luisa|||0000-0001-8069-3086, Martorell Alsina, Sebastián Salvador|||0000-0003-1706-4740
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/88159
Acceso en línea:https://riunet.upv.es/handle/10251/88159
Access Level:acceso abierto
Palabra clave:7Be
Time series
Forecasting
SARIMA model
ESTADISTICA E INVESTIGACION OPERATIVA
INGENIERIA NUCLEAR
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spelling Forecasting 7BE concentrations in surface air using time series analysisBas Cerdá, María del CarmenOrtiz Moragón, JosefinaBallesteros Pascual, Luisa|||0000-0001-8069-3086Martorell Alsina, Sebastián Salvador|||0000-0003-1706-47407BeTime seriesForecastingSARIMA modelESTADISTICA E INVESTIGACION OPERATIVAINGENIERIA NUCLEAR[EN] 7Be is a cosmogenic radionuclide widely used as an atmospheric tracer, whose evaluation and forecasting can provide valuable information on changes in the atmospheric behavior. In this study, measurements of 7Be concentrations were made each month during the period 2007-2015 from samples of atmospheric aerosols filtered from the air. The aim was to propose a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to develop an explanatory and predictive model of 7Be air concentrations. The Root Mean Square Error (RMSE) and the Adapted Mean Absolute Percentage Error (AMAPE) were selected to measure forecasting accuracy in identifying the best historical data time window to explain 7Be concentrations. A measure based on the variance of forecast errors was calculated to determine the impact of the model uncertainty on forecasts. We concluded that the SARIMA method is a powerful explanatory and predictive technique for explaining 7 Be air concentrations in a longterm series of at least eight years of historical data to forecast 7 Be concentration trends up to one year in advance.This study has been supported partially by the REM program of the Nuclear Safety Council of Spain (SRA/2071/2015/227.06).ElsevierDepartamento de Ingeniería Química y NuclearEscuela Técnica Superior de Ingeniería IndustrialGrupo de Medioambiente y Seguridad Industrial. MEDASEGIConsejo de Seguridad NuclearRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-02-14journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/88159reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengConsejo de Seguridad Nuclear https://doi.org/10.13039/501100006055 SRA%2F2071%2F2015%2F227.06open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/881592026-06-13T07:49:27Z
dc.title.none.fl_str_mv Forecasting 7BE concentrations in surface air using time series analysis
title Forecasting 7BE concentrations in surface air using time series analysis
spellingShingle Forecasting 7BE concentrations in surface air using time series analysis
Bas Cerdá, María del Carmen
7Be
Time series
Forecasting
SARIMA model
ESTADISTICA E INVESTIGACION OPERATIVA
INGENIERIA NUCLEAR
title_short Forecasting 7BE concentrations in surface air using time series analysis
title_full Forecasting 7BE concentrations in surface air using time series analysis
title_fullStr Forecasting 7BE concentrations in surface air using time series analysis
title_full_unstemmed Forecasting 7BE concentrations in surface air using time series analysis
title_sort Forecasting 7BE concentrations in surface air using time series analysis
dc.creator.none.fl_str_mv Bas Cerdá, María del Carmen
Ortiz Moragón, Josefina
Ballesteros Pascual, Luisa|||0000-0001-8069-3086
Martorell Alsina, Sebastián Salvador|||0000-0003-1706-4740
author Bas Cerdá, María del Carmen
author_facet Bas Cerdá, María del Carmen
Ortiz Moragón, Josefina
Ballesteros Pascual, Luisa|||0000-0001-8069-3086
Martorell Alsina, Sebastián Salvador|||0000-0003-1706-4740
author_role author
author2 Ortiz Moragón, Josefina
Ballesteros Pascual, Luisa|||0000-0001-8069-3086
Martorell Alsina, Sebastián Salvador|||0000-0003-1706-4740
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Química y Nuclear
Escuela Técnica Superior de Ingeniería Industrial
Grupo de Medioambiente y Seguridad Industrial. MEDASEGI
Consejo de Seguridad Nuclear
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv 7Be
Time series
Forecasting
SARIMA model
ESTADISTICA E INVESTIGACION OPERATIVA
INGENIERIA NUCLEAR
topic 7Be
Time series
Forecasting
SARIMA model
ESTADISTICA E INVESTIGACION OPERATIVA
INGENIERIA NUCLEAR
description [EN] 7Be is a cosmogenic radionuclide widely used as an atmospheric tracer, whose evaluation and forecasting can provide valuable information on changes in the atmospheric behavior. In this study, measurements of 7Be concentrations were made each month during the period 2007-2015 from samples of atmospheric aerosols filtered from the air. The aim was to propose a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to develop an explanatory and predictive model of 7Be air concentrations. The Root Mean Square Error (RMSE) and the Adapted Mean Absolute Percentage Error (AMAPE) were selected to measure forecasting accuracy in identifying the best historical data time window to explain 7Be concentrations. A measure based on the variance of forecast errors was calculated to determine the impact of the model uncertainty on forecasts. We concluded that the SARIMA method is a powerful explanatory and predictive technique for explaining 7 Be air concentrations in a longterm series of at least eight years of historical data to forecast 7 Be concentration trends up to one year in advance.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-02-14
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/88159
url https://riunet.upv.es/handle/10251/88159
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Consejo de Seguridad Nuclear https://doi.org/10.13039/501100006055 SRA%2F2071%2F2015%2F227.06
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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