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Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model

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Abstract

Air pollution imposed by particle matter (PM) made it a public health concern and hazard to humans and the environment. Reduced vision, allergic responses, pneumonia, asthma, cardiovascular disorders, lung cancer, and even mortality can result from prolonged exposure to the concentration of air’s small particulate matter. Air quality prediction can offer reliable information for future air pollution status to operate air pollution control effectively and make preventative plans. Tracking, predicting, and regulating emissions is crucial. Controlling PM2.5 is the key for enhancing air quality, and it can be accomplished by forecasting PM2.5 concentrations. This work develops a methodology for forecasting PM2.5 concentrations using a gradient-boosted regression tree with Convolutional Neural Network (CNN) and fuzzy K-nearest neighbour (fuzzy-KNN). The results of the proposed methodology have been comparatively analysed with multiple linear regression, stacked long short-term memory, bidirectional gated recurrent unit, and gradient-boosted regression tree. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are evaluated, and it shows that the gradient-boosted regression tree model produces a reduced error with improved accuracy in forecasting air quality.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRMIST Ramapuram Campus, Tamil Nadu, India

    A. Usha Ruby

  2. Director, Gullas College of Medicine, Cebu, Bengaluru, Philippines

    J. George Chellin Chandran

  3. Department of Computer Science and Engineering, GITAM University, 561203, Bengaluru, India

    Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya & T. J. Swasthika Jain

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  1. A. Usha Ruby
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  2. J. George Chellin Chandran
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  6. T. J. Swasthika Jain
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Contributions

Data collection and experimental works: A.Usha Ruby and J.George Chellin Chandran

Writing, discussion, analysis: Prasannavenkatesan Theerthagiri, Renuka Patil, Chaithanya B.N., and Swasthika Jain T.J.

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Correspondence to Prasannavenkatesan Theerthagiri.

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Usha Ruby, A., Chandran, J.G., Theerthagiri, P. et al. Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model. Opt. Mem. Neural Networks 33, 86–96 (2024). https://doi.org/10.3103/S1060992X24010107

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