Air Quality Prediction - A Study Using Neural Network Based Approach

Document Type : Regular Article

Authors

1 School of Planning & Architecture, Bhopal, Madhya Pradesh, 462030, India

2 National Institute of Technical Teachers’ Training and Research, Bhopal, Madhya Pradesh, 462002, India

3 Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

4 Structural Engineering Department, CSIR—Central Building Research Institute, Roorkee 247667, India

5 Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India

6 Department of Civil Engineering, University of Birjand, Birjand, 9717434765, Iran

Abstract

India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5) concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets for ten contaminants (PM2.5, PM10, NO, NO2, NH3, CO, SO2, O3, C6H6 and C7H8) were chosen. The datasets were collected throughout the last five years, from 2016 to 2020, and were used to develop the predictive model. Two machine learning model are proposing in this study namely Artificial Intelligence (AI) and Gaussian Process Regression (GPR) The R-value of ANN and GPR models are 0.9611 and 0.9843 sequentially. The other performance indices such as RMSE, MAPE, MAE of the GPR model are 21.4079, 7.8945% and 13.5884, respectively. The developed model is quite useful to update citizens about the predicted air quality of the urban spaces and protect them from getting affected by the poor ambient air quality. It can also be used to find the proper abatement strategies as well as operational measures.

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Main Subjects

References

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History

  • Receive Date: 16 July 2022
  • Revise Date: 30 November 2022
  • Accept Date: 26 December 2022
  • First Publish Date: 26 December 2022

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