Impacts of Meteorological Factors on PM<sub>10</sub>: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches

ÖZDEMİR U., ÇANKAYA S.

ENVIRONMENTAL FORENSICS, sa.4, ss.329-336, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1080/15275922.2014.950774
  • Dergi Adı: ENVIRONMENTAL FORENSICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.329-336
  • Anahtar Kelimeler: multi-linear regression (MLR), air pollution, sensitivity analyses, artificial neural network (ANN), coarse particulate matter (PM10), meteorological factors, AIR-POLLUTION, URBAN AIR, MULTILAYER PERCEPTRON, OZONE CONCENTRATIONS, HOSPITAL ADMISSIONS, PRINCIPAL COMPONENT, NO2 CONCENTRATIONS, SO2 CONCENTRATION, AQUEOUS-SOLUTION, PREDICTION
  • Kocaeli Üniversitesi Adresli: Evet

Özet

In this study, prediction capacities of multi-linear regression (MLR) and artificial neural networks (ANN) onto coarse particulate matter (PM10) concentrations were investigated. Different meteorological factors on particulate pollution were chosen for operating variables in the model analyses. Two different regions (urban and industrial) were identified in the region of Kocaeli, Turkey. All data sets were obtained from air quality monitoring network of the Ministry of Environment and Urban Planning, and 120 data sets were used in the MLR and ANN models. Regression equations explained the effects of the meteorological factors in MLR analyses. In the ANN model, backpropagation network with two hidden layers has achieved the best prediction efficiency. Determination coefficients and error values were examined for each model. ANN models displayed more accurate results compared to MLR.