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LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Ghaemi, Z.; Alimohammadi, A. and Farnaghi, M. LU (2018) In Environmental Monitoring and Assessment 190(5).
Abstract

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution... (More)

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Big data, LaSVM, Online prediction, Spatio-temporal, Tehran, Urban air quality
in
Environmental Monitoring and Assessment
volume
190
issue
5
publisher
Springer
external identifiers
  • scopus:85046259229
ISSN
0167-6369
DOI
10.1007/s10661-018-6659-6
language
English
LU publication?
yes
id
5056f742-df9d-4b5a-b630-bb909f3ab9c2
date added to LUP
2018-05-15 13:42:21
date last changed
2018-05-29 10:30:40
@article{5056f742-df9d-4b5a-b630-bb909f3ab9c2,
  abstract     = {<p>Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.</p>},
  articleno    = {300},
  author       = {Ghaemi, Z. and Alimohammadi, A. and Farnaghi, M.},
  issn         = {0167-6369},
  keyword      = {Big data,LaSVM,Online prediction,Spatio-temporal,Tehran,Urban air quality},
  language     = {eng},
  month        = {05},
  number       = {5},
  publisher    = {Springer},
  series       = {Environmental Monitoring and Assessment},
  title        = {LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran},
  url          = {http://dx.doi.org/10.1007/s10661-018-6659-6},
  volume       = {190},
  year         = {2018},
}