ISSN :2582-9793
Gautam Srivastava and Ramlah Abdulmalik
Adv. Artif. Intell. Mach. Learn., 3 (3):1295-1312
Gautam Srivastava : Brandon University
Ramlah Abdulmalik : Brandon University
DOI: 10.54364/AAIML.2023.1176
Article History: Received on: 10-May-23, Accepted on: 27-Jul-23, Published on: 04-Aug-23
Corresponding Author: Gautam Srivastava
Email: srivastavag@brandonu.ca
Citation: Ramlah Abdulmalik and Gautam Srivastava (2023). Forecasting of Transportation-related CO2 Emissions in Canada with Different Machine Learning Algorithms. Adv. Artif. Intell. Mach. Learn., 3 (3 ):1295-1312
The amount of carbon dioxide in the atmosphere has risen over recent years, with a growth of over 40%. This study examines transportation-related carbon dioxide (CO2) emissions in Canada, which contribute significantly to the country's overall emissions. The study investigates the rise of carbon dioxide (CO2) due to various reasons such as economic development, transportation and population growth but the study focuses on transportation related CO2 emission in Canada. Various machine learning algorithms, such as Deep Neural Networks, Support Vector Machines, and Random Forests, are utilized to forecast CO2 emissions. The results show promising outcomes, with R2 values ranging from 0.9532 to 0.9996, RMSE value ranging from 1.0974 to 13.6561, MAPE scores from 0.0088 to 0.0010, MBE score ranging from -0.0594 to 1.0366, rRMSE score ranging from 0.4259 to 5.3002, and MABE score ranging from 0.2643 to 5.6582 for all six (6) algorithms. To meet greenhouse gas reduction targets, the paper recommends further efforts to reduce CO2 emissions from transportation sources and suggests the adoption of Vehicle Alternative Fuel Types and low-carbon fuels.