Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial
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...
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| Autores principales: | , |
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| Formato: | Monografía o trabajo académico |
| Lenguaje: | español |
| Publicado: |
Universidad Peruana Unión
2024
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| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881510343351468032 |
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| author | Guerra Bendezu, Carlos Andres Romani Franco, Vivian Isabel |
| author2 | Lopez Gonzales, Javier Linkolk |
| author_browse | Guerra Bendezu, Carlos Andres Lopez Gonzales, Javier Linkolk Romani Franco, Vivian Isabel |
| author_facet | Lopez Gonzales, Javier Linkolk Guerra Bendezu, Carlos Andres Romani Franco, Vivian Isabel |
| author_sort | Guerra Bendezu, Carlos Andres |
| collection | Repositorio institucional |
| 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. |
| format | info:eu-repo/semantics/monograph |
| id | oai:repositorio.upeu.edu.pe:20.500.12840/7716 |
| institution | UPeU |
| language | Spanish |
| ocde_area_str_mv | Ciencias naturales |
| ocde_disciplina_str_mv | Matemáticas |
| ocde_str_mv | 1.01.03 |
| programa_nombre_str_mv | Segunda Especialidad en Estadística Aplicada para Investigación |
| programa_uri_str_mv | http://upeu.edu.pe/sys/programas/programa/seg-estadistica-aplicada-para-investigacion |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Universidad Peruana Unión |
| publisherStr | Universidad Peruana Unión |
| record_format | dspace |
| spelling | Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial Guerra Bendezu, Carlos Andres Romani Franco, Vivian Isabel Lopez Gonzales, Javier Linkolk Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting 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. 2024-04-15 2024-07-14T18:12:09Z 2024-07-14T18:12:09Z 2026-04-15 info:eu-repo/date/embargoEnd/2026-04-15 info:eu-repo/semantics/monograph https://hdl.handle.net/20.500.12840/7716 spa info:eu-repo/semantics/embargoedAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Universidad Peruana Unión PE oai:repositorio.upeu.edu.pe:20.500.12840/7716 2026-11-10T17:45:09Z |
| spellingShingle | Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting Guerra Bendezu, Carlos Andres Romani Franco, Vivian Isabel 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 |
| 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_short | Enfoque predictivo para la concentración de contaminante del aire basado en un modelo de red neuronal artificial |
| title_sort | enfoque predictivo para la concentracion de contaminante del aire basado en un modelo de red neuronal artificial |
| topic | Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting |
| topic_facet | Air pollution Hybrid methodology Artificial Neural Networks Time series Forecasting |
| url | https://hdl.handle.net/20.500.12840/7716 |
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