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ISSN:2325-6192 (print)
ISSN:2325-6206 (online)
Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal

Email: aml@fe.up.pt

Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

Fax: +86 29 82668723 Email: jzzhang@mail.xjtu.edu.cn

Comparative Analysis of Tree, Meta-learning and Function Classifiers to Predict the Atmospheric Concentration of NO2

Journal of Environmental Accounting and Management 8(1) (2020) 31--39 | DOI:10.5890/JEAM.2020.03.003

Adven Masih

Department of System Analysis and Decision Making, Ural Federal University, 19 Mira, Ekaterinburg, 620002, Sverdlovskaya oblast, Russian Federation

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Abstract

The concentration of airborne pollutants is rising in recent years. Due to serious health effects of NO2, SO2 etc. their constant monitoring is important for the policy makers, as it provides early pollution estimates before it crosses permissible limits set by the state. For air quality modelling, several statistical techniques based on Artificial Neural Networks have been applied, however, Tree and meta-learning based classifiers have rarely been adopted for air pollution prediction purpose. Thus, for this study, Tree (Random Forest, Reduces Error Pruning (REP) Tree), meta-learning (Bagging, Random Subspace) and Function (Multilayer Perceptron and Support Vector Machine) based classifiers have been employed to predict atmospheric concentrations of Nitrogen dioxide (NO2). The study uses 3 atmospheric pollutants; Sulphur dioxide (SO2), Carbon monoxide (CO), and Hydrochloric acid (HCl) and 5 meteorological parameters temperature, humidity, wind speed, wind direction and atmospheric pressure. Moreover, for validation of prediction models the performance of different classifiers were compared. The results obtained suggest that Tree classifiers in general and Random Forest in particular, can outperform Function (MLP and SVM) and meta-learning (Bagging and Random Subspace) classifiers to predict the atmospheric concentration of NO2.

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