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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Data Fusion from Multiple Stations for Estimation of PM2.5 in Specific Geographical Location

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

Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Data Fusion from Multiple Stations for Estimation of PM2.5 in Specific Geographical Location
verfasst von
Miguel A. Becerra
Marcela Bedoya Sánchez
Jacobo García Carvajal
Jaime A. Guzmán Luna
Diego H. Peluffo-Ordóñez
Catalina Tobón
Copyright-Jahr
2017
Buch
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Print ISBN: 978-3-319-52276-0

Electronic ISBN: 978-3-319-52277-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-52277-7_52