Machine learning model development for predicting road transport GHG emissions in Canada

Khan Mohd Jawad Ur Rehman and Awasthi Anjali ()
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Khan Mohd Jawad Ur Rehman: Concordia Institute for Information Systems Engineering (CIISE), Faculty of Engineering & Computer Science, Concordia University, Montreal, Quebec H3G 1M
Awasthi Anjali: Concordia Institute for Information Systems Engineering (CIISE), Faculty of Engineering & Computer Science, Concordia University, Montreal, Quebec H3G 1M

WSB Journal of Business and Finance, 2019, vol. 53, issue 2, 55-72

Abstract: Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

Keywords: Supervised machine learning; Feature selection; Multiple Linear Regression; Multilayer Perceptron; Ensemble learning; Bagging (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:wsbjbf:v:53:y:2019:i:2:p:55-72:n:1006

DOI: 10.2478/wsbjbf-2019-0022

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