Skip to main content

Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

  • Authors (legacy)
    Corresponding: Zeynep Ceylan
    Co-authors: Ceylan Z. and Bulkan S.
    Download PDF
  • gnest_02522_published.pdf
  • Paper ID
    gnest_02522
  • Paper status
    Published
  • Date paper submitted
  • Date paper accepted
  • Date paper online
DOI
https://doi.org/10.30955/gnj.002522
Abstract

In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels

Keywords
particulate matter PM10 Prediction Artificial Neural Network multi-linear regression
Cite this article
Bulkan, S. and Ceylan, Z. (2018) “Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City”, Global NEST Journal, 20(2). Available at: https://doi.org/10.30955/gnj.002522.
PDF file
  • gnest_02522_published.pdf