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

doi:10.1007/978-3-319-52277-7_52

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

Grupo de Investigación en Materiales Nanoestructurados y Biomodelación

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia

2 Citas (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 original	Inglés estadounidense
Título de la publicación alojada	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
Páginas	426-433
Número de páginas	8
ISBN (versión digital)	9783319522760
DOI	https://doi.org/10.1007/978-3-319-52277-7_52
Estado	Publicada - 1 ene. 2017
Evento	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duración: 1 ene. 2017 → …

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	10125 LNCS
ISSN (versión impresa)	0302-9743

Conferencia

Conferencia	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período	1/01/17 → …

Tipos de productos de Minciencias

Artículo A2 - Q2

ODS de las Naciones Unidas

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

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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 et al. / 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)).

@inproceedings{51ea1b7a735a4c10b0b1627ab0943047,

title = "Data fusion from multiple stations for estimation of PM2.5 in specific geographical location",

abstract = "{\textcopyright} 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 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 et al.
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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia

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

T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Y2 - 1 January 2017

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)). doi: 10.1007/978-3-319-52277-7_52

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

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