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 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. © Springer International Publishing AG 2017.

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