Air Pollution Evaluation by Combining Stationary, Smart Mobile Pollution Monitoring and Data-Driven Modelling

Authors

  • A. Shifa  ME Student, Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India
  • Dr. S. Rathi  Professor, Department of Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT217331

Keywords:

Air Pollution, AQI value, Neural Networks, Support Vector Regression, R2 value

Abstract

Air pollution has become a major issue in large cities because increasing traffic, industrialization and it becomes more difficult to manage due to its hazardous effects on the human health and many air pollution-triggering factors. This paper puts forth a machine learning approach to evaluate the accuracy and potential of such mobile generated information for prediction of air pollution. Temperature, wind, humidity play a vital role in influencing the pollution dispersion and accumulation, majorly influencing the prediction of pollution levels. Thus, this paper includes the atmospheric condition information registered throughout the study period in order to understand the influence of these factors on air pollution monitoring. Data driven modelling is an efficient way of extracting valuable information from generated data sets, however it is less efficient when the data is incomplete or contains inaccuracies. This modelling approach has true potential for real time operations because it can detect non-linear spatial relationships between sensing units and could aggregate results for regional investigation. Neural networks comparatively showed good capability in air quality prediction than support vector regression.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

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How to Cite

[1]
A. Shifa, Dr. S. Rathi, " Air Pollution Evaluation by Combining Stationary, Smart Mobile Pollution Monitoring and Data-Driven Modelling, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.222-227, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT217331