Data fusion from multiple stations for estimation of PM2.5 in specific geographical location

Miguel A. Becerra, Marcela Bedoya Sánchez, Jacobo García Carvajal, Jaime A.Guzmán Luna, Diego H. Peluffo-Ordóñez, Catalina Tobón

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciaInvestigación

1 Cita (Scopus)

Resumen

© Springer International Publishing AG 2017. 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.
Idioma originalInglés estadounidense
Título de la publicación alojadaData fusion from multiple stations for estimation of PM2.5 in specific geographical location
Páginas426-433
Número de páginas8
ISBN (versión digital)9783319522760
DOI
EstadoPublicada - 1 ene 2017
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2017 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10125 LNCS
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/17 → …

Huella dactilar

Data Fusion
Data fusion
Support Vector Regression
Fuzzy Inference System
Fuzzy inference
Estimate
Air Quality
Selector
Public Health
Public health
Pollutants
Air
Rough Set
Contamination
Air quality
Feature Selection
Registration
Feature extraction
Monitoring
Decrease

Citar esto

Becerra, M. A., Sánchez, M. B., Carvajal, J. G., Luna, J. A. G., Peluffo-Ordóñez, D. H., & Tobón, C. (2017). Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. En Data fusion from multiple stations for estimation of PM2.5 in specific geographical location (pp. 426-433). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10125 LNCS). https://doi.org/10.1007/978-3-319-52277-7_52
Becerra, Miguel A. ; Sánchez, Marcela Bedoya ; Carvajal, Jacobo García ; Luna, Jaime A.Guzmán ; Peluffo-Ordóñez, Diego H. ; Tobón, Catalina. / Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. 2017. pp. 426-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Data fusion from multiple stations for estimation of PM2.5 in specific geographical location",
abstract = "{\circledC} Springer International Publishing AG 2017. 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 Aburr{\'a} (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.",
author = "Becerra, {Miguel A.} and S{\'a}nchez, {Marcela Bedoya} and Carvajal, {Jacobo Garc{\'i}a} and Luna, {Jaime A.Guzm{\'a}n} and Peluffo-Ord{\'o}{\~n}ez, {Diego H.} and Catalina Tob{\'o}n",
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doi = "10.1007/978-3-319-52277-7_52",
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Becerra, MA, Sánchez, MB, Carvajal, JG, Luna, JAG, Peluffo-Ordóñez, DH & Tobón, C 2017, Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. En Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10125 LNCS, pp. 426-433, 1/01/17. https://doi.org/10.1007/978-3-319-52277-7_52

Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. / Becerra, Miguel A.; Sánchez, Marcela Bedoya; Carvajal, Jacobo García; Luna, Jaime A.Guzmán; Peluffo-Ordóñez, Diego H.; Tobón, Catalina.

Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. 2017. p. 426-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10125 LNCS).

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciaInvestigación

TY - GEN

T1 - Data fusion from multiple stations for estimation of PM2.5 in specific geographical location

AU - Becerra, Miguel A.

AU - Sánchez, Marcela Bedoya

AU - Carvajal, Jacobo García

AU - Luna, Jaime A.Guzmán

AU - Peluffo-Ordóñez, Diego H.

AU - Tobón, Catalina

PY - 2017/1/1

Y1 - 2017/1/1

N2 - © Springer International Publishing AG 2017. 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.

AB - © Springer International Publishing AG 2017. 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.

U2 - 10.1007/978-3-319-52277-7_52

DO - 10.1007/978-3-319-52277-7_52

M3 - Conference contribution

SN - 9783319522760

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 426

EP - 433

BT - Data fusion from multiple stations for estimation of PM2.5 in specific geographical location

ER -

Becerra MA, Sánchez MB, Carvajal JG, Luna JAG, Peluffo-Ordóñez DH, Tobón C. Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. En Data fusion from multiple stations for estimation of PM2.5 in specific geographical location. 2017. p. 426-433. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-52277-7_52