Traffic related PM predictor for besiktas, Turkey

Ferhat Karaca, Ismail Anil, Omar Alagha, Fatih Camci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


The main objective of this study was to develop an Artificial Neural Networks (ANN) based model, which could be used as a tool for the prediction of traffic related PM2.5 and PM10 emissions. In this purpose, about 70 pairs of daily PM2.5 and PM2.5-10 samples were collected near to a main artery in Besiktas, Istanbul, Turkey. In addition to the PM data, hourly meteorological data, air quality data (CO, SO2, NO, NO2, NOx) and traffic data (traffic counts, speed, and density) were employed in the model. The results obtained from two different Neural Networks namely Forward NN (FFNN) and Radial Basis Function NN (RBFNN) were compared. While FFNN did not give good results due to limited number of data (60% of 70 data points) in high dimensional space (i.e., 14 dimensional space), more robust results were obtained with RBFNN with 72% prediction performance.

Original languageEnglish
Title of host publicationInformation Technologies in Environmental Engineering - Proceedings of the 4th International ICSC Symposium, ITEE 2009
PublisherKluwer Academic Publishers
Number of pages14
ISBN (Print)9783540883500
Publication statusPublished - Jan 1 2009
Externally publishedYes
Event4th International ICSC Symposium on Information Technologies in Environmental Engineering, ITEE 2009 - Thessaloniki, Greece
Duration: May 28 2009May 29 2009

Publication series

NameEnvironmental Science and Engineering (Subseries: Environmental Science)
ISSN (Print)1863-5520


Conference4th International ICSC Symposium on Information Technologies in Environmental Engineering, ITEE 2009


  • Air pollution prediction
  • Air quality management
  • Fine and coarse particles
  • Inhalable particles
  • Monitoring

ASJC Scopus subject areas

  • Information Systems
  • Environmental Engineering


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