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01-03-2024

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

Authors: A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya, T. J. Swasthika Jain

Published in: Optical Memory and Neural Networks | Issue 1/2024

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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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Title: Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model
Authors: A. Usha Ruby
J. George Chellin Chandran
Prasannavenkatesan Theerthagiri
Renuka Patil
B. N. Chaithanya
T. J. Swasthika Jain
Publication date: 01-03-2024
Publisher: Pleiades Publishing
Published in: Optical Memory and Neural Networks / Issue 1/2024
Print ISSN: 1060-992X
Electronic ISSN: 1934-7898
DOI: https://doi.org/10.3103/S1060992X24010107

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