Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana
Particulate matter (PM) is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 are pollutant particles with a diameter of less than 10 micrometers. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Tesis de pregrado |
| Lenguaje: | inglés |
| Publicado: |
Universidad Peruana Unión
2025
|
| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881763688277344256 |
|---|---|
| asesor_orcid_str_mv | 0000-0003-0847-0552 |
| asesor_str_mv | Lopez Gonzales, Javier Linkolk |
| author | Solis Teran, Miguel Angel |
| author2 | Lopez Gonzales, Javier Linkolk |
| author_browse | Lopez Gonzales, Javier Linkolk Solis Teran, Miguel Angel |
| author_facet | Lopez Gonzales, Javier Linkolk Solis Teran, Miguel Angel |
| author_sort | Solis Teran, Miguel Angel |
| collection | Repositorio institucional |
| comunidad_str_mv | Facultad de Ingeniería y Arquitectura 3. Licenciatura Tesis y disertaciones |
| description | Particulate matter (PM) is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 are pollutant particles with a diameter of less than 10 micrometers. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM10 data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the availablePM10 data and evaluate the quality of time series classical models and neural networks for short-term forecasting. The Box-Jenkins models showed the best performance for short-term forecasting compared to the neural network models considered. |
| escuela_str_mv | Escuela Profesional de Ingeniería de Sistemas |
| format | info:eu-repo/semantics/bachelorThesis |
| id | oai:repositorio.upeu.edu.pe:20.500.12840/8559 |
| institution | UPeU |
| jurado_str_mv | Cuellar Rodriguez, Immer Elias Asin Gomez, Fernando Manuel Saboya Rios, Nemias Orrego Granados, David Leandro Lopez Gonzales, Javier Linkolk |
| language | English |
| linea_str_mv | Inteligencia artificial |
| ocde_area_str_mv | Ciencias naturales |
| ocde_disciplina_str_mv | Matemáticas |
| ocde_str_mv | 1.01.03 |
| programa_nombre_str_mv | Ingeniería de Sistemas |
| programa_pxx_str_mv | P27 |
| programa_uri_str_mv | http://upeu.edu.pe/sys/programas/programa/ingenieria-de-sistemas |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Universidad Peruana Unión |
| publisherStr | Universidad Peruana Unión |
| record_format | dspace |
| sede_str_mv | Lima |
| spelling | Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana Solis Teran, Miguel Angel Lopez Gonzales, Javier Linkolk Neural network Modeling Artificial intelligence Particulate matter (PM) is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 are pollutant particles with a diameter of less than 10 micrometers. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM10 data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the availablePM10 data and evaluate the quality of time series classical models and neural networks for short-term forecasting. The Box-Jenkins models showed the best performance for short-term forecasting compared to the neural network models considered. 2025-02-24 2025-03-14T13:58:13Z 2025-03-14T13:58:13Z 2027-02-24 info:eu-repo/date/embargoEnd/2027-02-24 info:eu-repo/semantics/bachelorThesis https://hdl.handle.net/20.500.12840/8559 eng 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:20.500.12840/8559 2026-11-10T22:57:48Z com_20.500.12840_44 Facultad de Ingeniería y Arquitectura com_20.500.12840_8 3. Licenciatura com_20.500.12840_5 Tesis y disertaciones col_20.500.12840_73 Ingeniería de Sistemas |
| spellingShingle | Neural network Modeling Artificial intelligence Solis Teran, Miguel Angel Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana |
| title | Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana |
| title_full | Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana |
| title_fullStr | Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana |
| title_full_unstemmed | Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana |
| title_short | Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana |
| title_sort | modelado basado en redes neuronales artificiales memoria de largo corto plazo para la contaminacion en lima metropolitana |
| topic | Neural network Modeling Artificial intelligence |
| topic_facet | Neural network Modeling Artificial intelligence |
| url | https://hdl.handle.net/20.500.12840/8559 |
| work_keys_str_mv | AT solisteranmiguelangel modeladobasadoenredesneuronalesartificialesmemoriadelargocortoplazoparalacontaminacionenlimametropolitana |