Prediction of effect of wind speed on air pollution level using machine learning technique
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Anuradha Pandey
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Nekram Rawal
Abstract
Air pollution is one of the most challenging issues poses serious threat to human health and environment. The increasing influx of population in metropolitan cities has further worsened the situation. Quantifying the air pollution experimentally is quite a challenging task as it depends on many parameters viz., wind speed, wind temperature, relative humidity, temperature etc. It requires the investment of huge money and manpower for controlling air pollution. Machine learning technique-based computer modelling reduces both of the parameters. In the present work, the dependence of air pollution level on wind speed and temperature has been taken up using machine learning in the form of ANN and LSTM model. The recorded data of air pollution level (PM2.5) is collected from a measurement station of Lucknow city situated at Central School, CPCB. The data is used in an Artificial Neural based network and in an LSTM model to predict suitably the level of air pollution for a known value of average wind speed and temperature without experimental measurements. LSTM model is found to predict the pollution level better than ANN for the developed ANN networks.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
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- A review of frictional pressure drop characteristics of single phase microchannels having different shapes of cross sections
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- Enhanced design of PI controller with lead-lag filter for unstable and integrating plus time delay processes
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Artikel in diesem Heft
- Frontmatter
- Review
- A review of frictional pressure drop characteristics of single phase microchannels having different shapes of cross sections
- Research Articles
- Taguchi L16 (44) orthogonal array-based study and thermodynamics analysis for electro-Fenton process treatment of textile industrial dye
- Green synthesis of silver nanoparticles from Aspergillus flavus and their antibacterial performance
- Prediction of effect of wind speed on air pollution level using machine learning technique
- Model-based evaluation of heat of combustion using the degree of reduction
- Enhanced design of PI controller with lead-lag filter for unstable and integrating plus time delay processes
- Effect of operating parameters on the sludge settling characteristics by treatment of the textile dyeing effluent using electrocoagulation
- Simultaneous charging and discharging of metal foam composite phase change material in triplex-tube latent heat storage system under various configurations
- Optimal design of pressure swing adsorption units for hydrogen recovery under uncertainty
- Thermo-kinetics, thermodynamics, and ANN modeling of the pyrolytic behaviours of Corn Cob, Husk, Leaf, and Stalk using thermogravimetric analysis
