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.
KeywordsANFIS 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.
- 1.OMS | Calidad del aire (exterior) y salud, WHO. http://www.who.int/mediacentre/factsheets/fs313/es/. Accessed 24 Oct 2015
- 11.Velásquez, J.D., Olaya, Y., Franco, C.J.: Time series prediction using support vector machines. Ingeniare, 64–75 (2010)Google Scholar
- 18.Pai, T.-Y., Hanaki, K., Su, H.-C., Yu, L.-F.: A 24-h forecast of oxidant concentration in Tokyo using neural network and fuzzy learning approach. CLEAN – Soil Air. Water 41(8), 729–736 (2013)Google Scholar
- 19.Polat, K.: A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS). Neural Comput. Appl. 21(8), 1–8 (2001)Google Scholar
- 23.Orrego, D.A., Becerra, M.A., Delgado-Trejos, E.: Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5282–5285 (2012)Google Scholar
- 24.Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994)Google Scholar