Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial
Descripción del Articulo
In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with...
| Autores: | , |
|---|---|
| Formato: | tesis de grado |
| Fecha de Publicación: | 2024 |
| Institución: | Universidad Peruana Unión |
| Repositorio: | UPEU-Tesis |
| Lenguaje: | español |
| OAI Identifier: | oai:repositorio.upeu.edu.pe:20.500.12840/7716 |
| Enlace del recurso: | http://repositorio.upeu.edu.pe/handle/20.500.12840/7716 |
| Nivel de acceso: | acceso embargado |
| Materia: | Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting http://purl.org/pe-repo/ocde/ford#1.01.03 |
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| dc.title.none.fl_str_mv |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| title |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| spellingShingle |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial Guerra Bendezu, Carlos Andres Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting http://purl.org/pe-repo/ocde/ford#1.01.03 |
| title_short |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| title_full |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| title_fullStr |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| title_full_unstemmed |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| title_sort |
Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| author |
Guerra Bendezu, Carlos Andres |
| author_facet |
Guerra Bendezu, Carlos Andres Romani Franco, Vivian Isabel |
| author_role |
author |
| author2 |
Romani Franco, Vivian Isabel |
| author2_role |
author |
| dc.contributor.advisor.fl_str_mv |
Lopez Gonzales, Javier Linkolk |
| dc.contributor.author.fl_str_mv |
Guerra Bendezu, Carlos Andres Romani Franco, Vivian Isabel |
| dc.subject.none.fl_str_mv |
Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting |
| topic |
Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting http://purl.org/pe-repo/ocde/ford#1.01.03 |
| dc.subject.ocde.none.fl_str_mv |
http://purl.org/pe-repo/ocde/ford#1.01.03 |
| description |
In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves the performance metrics when forecasting daily extreme values of PM2.5. |
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2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-14T18:12:09Z |
| dc.date.available.none.fl_str_mv |
2024-07-14T18:12:09Z |
| dc.date.embargoEnd.none.fl_str_mv |
2026-04-15 |
| dc.date.issued.fl_str_mv |
2024-04-15 |
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info:eu-repo/semantics/bachelorThesis |
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http://repositorio.upeu.edu.pe/handle/20.500.12840/7716 |
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http://repositorio.upeu.edu.pe/handle/20.500.12840/7716 |
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spa |
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spa |
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SUNEDU |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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Universidad Peruana Unión |
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Universidad Peruana Unión |
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Lopez Gonzales, Javier LinkolkGuerra Bendezu, Carlos AndresRomani Franco, Vivian Isabel2024-07-14T18:12:09Z2024-07-14T18:12:09Z2024-04-152026-04-15http://repositorio.upeu.edu.pe/handle/20.500.12840/7716In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves the performance metrics when forecasting daily extreme values of PM2.5.application/pdfspaUniversidad Peruana UniónPEinfo:eu-repo/semantics/embargoedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Air pollutionHybrid methodologyArtificial Neural NetworksTime series Forecastinghttp://purl.org/pe-repo/ocde/ford#1.01.03Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificialinfo:eu-repo/semantics/bachelorThesisreponame:UPEU-Tesisinstname:Universidad Peruana Unióninstacron:UPEUSUNEDUSegunda Especialidad en Estadística Aplicada para InvestigaciónUniversidad Peruana Unión. Unidad de Posgrado de Ingeniería y ArquitecturaSegunda Especialidad Profesional de Ingeniería: Estadística para Investigación46071566https://orcid.org/0000-0003-0847-05520814492909726163542039Abanto Ramirez, Carlos Danielhttp://purl.org/pe-repo/renati/nivel#tituloSegundaEspecialidadhttp://purl.org/pe-repo/renati/type#tesisORIGINALVivian_Tesis_Esp_24.pdfVivian_Tesis_Esp_24.pdfapplication/pdf482416https://repositorio.upeu.edu.pe/bitstreams/69e7bea9-7457-4b62-b18e-95bdcec13ba1/download2c0cc42e6aed615191aef36771d370e9MD51Autorización.pdfAutorización.pdfapplication/pdf108481https://repositorio.upeu.edu.pe/bitstreams/9006ff30-6738-4c74-bc02-5fe164e0e392/downloadfe4521b75946e2ab59d57244f7107a94MD52Reporte de similitud.pdfReporte de similitud.pdfapplication/pdf2269538https://repositorio.upeu.edu.pe/bitstreams/e8ab91a4-9864-47c0-80a0-cbd259c732d0/download8fdd437cabbf13bc2332e8a94fe60cd9MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.upeu.edu.pe/bitstreams/28634fb6-4ed9-4619-8bc1-e8fb89ec043d/downloadbb9bdc0b3349e4284e09149f943790b4MD5420.500.12840/7716oai:repositorio.upeu.edu.pe:20.500.12840/77162024-08-20 18:00:52.517http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/embargoedAccessopen.accesshttps://repositorio.upeu.edu.peDSpace 7repositorio-help@upeu.edu.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 |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
