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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)


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.


ANFIS PM2.5 estimation Support Vector Regression 



This work was supported by the research project identified with code 267 at the “Institución Universitaria Salazar y Herrera” of Medellin, Colombia, CALAIRE Laboratory of “Universidad Nacional of Colombia”, and the Area Metropolitana de Medellín, who supplied the database.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miguel A. Becerra
    • 1
    • 2
  • Marcela Bedoya Sánchez
    • 1
  • Jacobo García Carvajal
    • 1
  • Jaime A. Guzmán Luna
    • 2
  • Diego H. Peluffo-Ordóñez
    • 3
    • 4
  • Catalina Tobón
    • 5
  1. 1.GEA Research Group, Institución Universitaria Salazar y HerreraMedellínColombia
  2. 2.SINTELWEB Research Group, Universidad Nacional de ColombiaMedellínColombia
  3. 3.Facultad de Ingeniería en Ciencias Aplicadas-FICA from Universidad Técnica del NorteIbarraEcuador
  4. 4.Department of ElectronicsUniversidad de NariñoPastoColombia
  5. 5.Universidad de MedellínMedellínColombia

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