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

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...

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Autores:

Tipo de recurso:

Fecha de publicación:

2017

Institución:

Universidad de Medellín

Repositorio:

Repositorio UDEM

Idioma:

eng

OAI Identifier:

oai:repository.udem.edu.co:11407/4277

Acceso en línea:

http://hdl.handle.net/11407/4277

Palabra clave:

ANFIS
PM2.5 estimation
Support vector regression
Air quality
Data fusion
Location
Pattern recognition
Public health
Adaptive neural fuzzy inference system (ANFIS)
Air quality networks
ANFIS
Contamination levels
Environmental database
Geographical locations
Meteorological variables
Support vector regression (SVR)
Fuzzy inference

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http://purl.org/coar/access_right/c_16ec