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

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Ceylan Z., BULKAN S.

GLOBAL NEST JOURNAL, vol.20, no.2, pp.281-290, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 2
  • Publication Date: 2018
  • Doi Number: 10.30955/gnj.002522
  • Journal Name: GLOBAL NEST JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.281-290
  • Keywords: Particulate matter, PM10, prediction, artificial neural network, multi-linear regression, ARTIFICIAL NEURAL-NETWORKS, AIR-POLLUTION, MODEL, PREDICTION, PM2.5, EXPOSURE
  • Ondokuz Mayıs University Affiliated: No

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.