Machine learning approach to forecasting urban pollution
This work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach. The study presents a decision tree al...
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| Format: | article |
| Language: | eng |
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2016
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| Online Access: | https://ieeexplore.ieee.org/document/7750810 https://hdl.handle.net/20.500.14809/3522 |
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| author | Rybarczyk, Yves |
| author2 | Zalakeviciute, Rasa |
| author2_role | author |
| author_facet | Rybarczyk, Yves Zalakeviciute, Rasa |
| author_role | author |
| collection | Repositorio Universidad Tecnológica Indoamérica |
| dc.creator.none.fl_str_mv | Rybarczyk, Yves Zalakeviciute, Rasa |
| dc.date.none.fl_str_mv | 2016 2022-07-02T17:01:57Z 2022-07-02T17:01:57Z |
| dc.identifier.none.fl_str_mv | https://ieeexplore.ieee.org/document/7750810 https://hdl.handle.net/20.500.14809/3522 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 |
| dc.rights.none.fl_str_mv | https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:Repositorio Universidad Tecnológica Indoamérica instname:Universidad Tecnológica Indoamérica instacron:UTI |
| dc.title.none.fl_str_mv | Machine learning approach to forecasting urban pollution |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach. The study presents a decision tree algorithm that learns to classify the concentrations of fine aerosols, into two categories (>15μg/m3 vs. <15μg/m3), from a limited number of parameters such as the level of precipitation and the wind speed and direction. Requiring few rules, the resulting models are able to infer the concentration outcome with significant accuracy. This fundamental research intends to be a preliminary step in the development of a web-based platform and smartphone app to alert the inhabitants of Ecuador's capital about the risk to human health, with potential future application in other urban areas. © 2016 IEEE. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | UTI_3a9dcce1a4e345d69f9e600e951ff7f7 |
| instacron_str | UTI |
| institution | UTI |
| instname_str | Universidad Tecnológica Indoamérica |
| language | eng |
| network_acronym_str | UTI |
| network_name_str | Repositorio Universidad Tecnológica Indoamérica |
| oai_identifier_str | oai:repositorio.uti.edu.ec:20.500.14809/3522 |
| publishDate | 2016 |
| publisher.none.fl_str_mv | 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 |
| reponame_str | Repositorio Universidad Tecnológica Indoamérica |
| repository.mail.fl_str_mv | . |
| repository.name.fl_str_mv | Repositorio Universidad Tecnológica Indoamérica - Universidad Tecnológica Indoamérica |
| repository_id_str | 0 |
| rights_invalid_str_mv | https://creativecommons.org/licenses/by/4.0/ |
| spelling | Machine learning approach to forecasting urban pollutionRybarczyk, YvesZalakeviciute, RasaThis work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach. The study presents a decision tree algorithm that learns to classify the concentrations of fine aerosols, into two categories (>15μg/m3 vs. <15μg/m3), from a limited number of parameters such as the level of precipitation and the wind speed and direction. Requiring few rules, the resulting models are able to infer the concentration outcome with significant accuracy. This fundamental research intends to be a preliminary step in the development of a web-based platform and smartphone app to alert the inhabitants of Ecuador's capital about the risk to human health, with potential future application in other urban areas. © 2016 IEEE.2016 IEEE Ecuador Technical Chapters Meeting, ETCM 20162022-07-02T17:01:57Z2022-07-02T17:01:57Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://ieeexplore.ieee.org/document/7750810https://hdl.handle.net/20.500.14809/3522enghttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessreponame:Repositorio Universidad Tecnológica Indoaméricainstname:Universidad Tecnológica Indoaméricainstacron:UTI2022-07-09T16:09:05Zoai:repositorio.uti.edu.ec:20.500.14809/3522Institucionalhttps://repositorio.uti.edu.ec/Institución privadahttps://indoamerica.edu.ec/https://repositorio.uti.edu.ec/oai.Ecuador...opendoar:02022-07-09T16:09:05Repositorio Universidad Tecnológica Indoamérica - Universidad Tecnológica Indoaméricafalse |
| spellingShingle | Machine learning approach to forecasting urban pollution Rybarczyk, Yves |
| status_str | publishedVersion |
| title | Machine learning approach to forecasting urban pollution |
| title_full | Machine learning approach to forecasting urban pollution |
| title_fullStr | Machine learning approach to forecasting urban pollution |
| title_full_unstemmed | Machine learning approach to forecasting urban pollution |
| title_short | Machine learning approach to forecasting urban pollution |
| title_sort | Machine learning approach to forecasting urban pollution |
| url | https://ieeexplore.ieee.org/document/7750810 https://hdl.handle.net/20.500.14809/3522 |