<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 &amp; Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.</p></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.

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

Concordia Institute for Information Systems Engineering (CIISE), Faculty of Engineering & Computer Science

WSB Journal of Business and Finance

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose...

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

Online veröffentlicht: 31. Dez. 2019

Seitenbereich: 55 - 72

DOI: https://doi.org/10.2478/wsbjbf-2019-0022

Schlüsselwörter
Supervised machine learning, Feature selection, Multiple Linear Regression, Multilayer Perceptron, Ensemble learning, Bagging

© 2019 Mohd Jawad Ur Rehman Khan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 31. Dez. 2019

Seitenbereich: 55 - 72

DOI: https://doi.org/10.2478/wsbjbf-2019-0022

SchlüsselwörterSupervised machine learning, Feature selection, Multiple Linear Regression, Multilayer Perceptron, Ensemble learning, Bagging

© 2019 Mohd Jawad Ur Rehman Khan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Supervised machine learning, Feature selection, Multiple Linear Regression, Multilayer Perceptron, Ensemble learning, Bagging