dc.creator | Becerra M.A. | spa |
dc.creator | Sánchez M.B. | spa |
dc.creator | Carvajal J.G. | spa |
dc.creator | Luna J.A.G. | spa |
dc.creator | Peluffo-Ordóñez D.H. | spa |
dc.creator | Tobón C. | spa |
dc.date.accessioned | 2017-12-19T19:36:44Z | |
dc.date.available | 2017-12-19T19:36:44Z | |
dc.date.created | 2017 | |
dc.identifier.isbn | 9783319522760 | |
dc.identifier.issn | 3029743 | |
dc.identifier.uri | http://hdl.handle.net/11407/4277 | |
dc.description.abstract | Nowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents a comparison among approaches based on Adaptive Neural Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for the estimation level of PM2.5 (Particle Material 2.5) in specific geographic locations based on nearby stations. The systems were validated using an environmental database that belongs to air quality network of Valle de Aburrá (AMVA) of Medellin Colombia, which has the registration of 5 meteorological variables and 2 pollutants that are from 3 nearby measurement stations. Therefore, this project analyses the relevance of the characteristics obtained in every single station to estimate the levels of PM2.5 in the target station, using four different selectors based on Rough Set Feature Selection (RSFS) algorithms. Additionally, five systems to estimate the PM2.5 were compared: three based on ANFIS, and two based on SVR to obtain an aim and an efficient mechanism to estimate the levels of PM2.5 in specific geographic locations fusing data obtained from the near monitoring stations. © Springer International Publishing AG 2017. | eng |
dc.language.iso | eng | |
dc.publisher | Springer Verlag | spa |
dc.relation.isversionof | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013471394&doi=10.1007%2f978-3-319-52277-7_52&partnerID=40&md5=668dac684d7746221537a90a90404d13 | spa |
dc.source | Scopus | spa |
dc.title | Data fusion from multiple stations for estimation of PM2.5 in specific geographical location | spa |
dc.type | Conference Paper | eng |
dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | |
dc.contributor.affiliation | Becerra, M.A., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia, SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombia | spa |
dc.contributor.affiliation | Sánchez, M.B., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia | spa |
dc.contributor.affiliation | Carvajal, J.G., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia | spa |
dc.contributor.affiliation | Luna, J.A.G., SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombia | spa |
dc.contributor.affiliation | Peluffo-Ordóñez, D.H., Facultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, Ecuador, Department of Electronics, Universidad de Nariño, Pasto, Colombia | spa |
dc.contributor.affiliation | Tobón, C., Universidad de Medellín, Medellín, Colombia | spa |
dc.identifier.doi | 10.1007/978-3-319-52277-7_52 | |
dc.subject.keyword | ANFIS | eng |
dc.subject.keyword | PM2.5 estimation | eng |
dc.subject.keyword | Support vector regression | eng |
dc.subject.keyword | Air quality | eng |
dc.subject.keyword | Data fusion | eng |
dc.subject.keyword | Location | eng |
dc.subject.keyword | Pattern recognition | eng |
dc.subject.keyword | Public health | eng |
dc.subject.keyword | Adaptive neural fuzzy inference system (ANFIS) | eng |
dc.subject.keyword | Air quality networks | eng |
dc.subject.keyword | ANFIS | eng |
dc.subject.keyword | Contamination levels | eng |
dc.subject.keyword | Environmental database | eng |
dc.subject.keyword | Geographical locations | eng |
dc.subject.keyword | Meteorological variables | eng |
dc.subject.keyword | Support vector regression (SVR) | eng |
dc.subject.keyword | Fuzzy inference | eng |
dc.publisher.faculty | Facultad de Ciencias Básicas | spa |
dc.abstract | Nowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents a comparison among approaches based on Adaptive Neural Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for the estimation level of PM2.5 (Particle Material 2.5) in specific geographic locations based on nearby stations. The systems were validated using an environmental database that belongs to air quality network of Valle de Aburrá (AMVA) of Medellin Colombia, which has the registration of 5 meteorological variables and 2 pollutants that are from 3 nearby measurement stations. Therefore, this project analyses the relevance of the characteristics obtained in every single station to estimate the levels of PM2.5 in the target station, using four different selectors based on Rough Set Feature Selection (RSFS) algorithms. Additionally, five systems to estimate the PM2.5 were compared: three based on ANFIS, and two based on SVR to obtain an aim and an efficient mechanism to estimate the levels of PM2.5 in specific geographic locations fusing data obtained from the near monitoring stations. © Springer International Publishing AG 2017. | eng |
dc.creator.affiliation | GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia | spa |
dc.creator.affiliation | SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombia | spa |
dc.creator.affiliation | Facultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, Ecuador | spa |
dc.creator.affiliation | Department of Electronics, Universidad de Nariño, Pasto, Colombia | spa |
dc.creator.affiliation | Universidad de Medellín, Medellín, Colombia | spa |
dc.relation.ispartofes | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | spa |
dc.relation.ispartofes | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 10125 LNCS, 2017, Pages 426-433 | spa |
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dc.type.version | info:eu-repo/semantics/publishedVersion | |
dc.type.driver | info:eu-repo/semantics/conferenceObject | |
dc.identifier.reponame | reponame:Repositorio Institucional Universidad de Medellín | spa |
dc.identifier.instname | instname:Universidad de Medellín | spa |