TY - JOUR
T1 - Forecasting Carbon Dioxide Emissions of Light-Duty Vehicles with Different Machine Learning Algorithms
AU - Natarajan, Yuvaraj
AU - Wadhwa, Gitanjali
AU - Sri Preethaa, K. R.
AU - Paul, Anand
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/5
Y1 - 2023/5
N2 - Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the government of Canada has collected a large sensor-based dataset containing detailed information on 7384 light-duty vehicles from 2017 to 2021, with the goal of reducing CO2 emissions by 40–45% by 2030. To this end, various researchers worldwide have developed vehicle emissions and consumption models to comply with these targets and achieve the Canadian government’s ambitious objectives. In this work, we propose the development of boosting and other regression models to predict carbon dioxide emissions for light-duty vehicle designs, with the aim of creating ensemble learning models that leverage vehicle specifications to forecast emissions. Our proposed boosting model is capable of accurately predicting CO2 emissions, even with only one car attribute as input. Moreover, our regression models, in conjunction with the boosting algorithm, can effectively make predictions from various vehicle inputs. Our proposed technique, categorical boosting (Catboost), provides critical insights into transportation-generated air pollution, offering valuable recommendations for both vehicle users and manufacturers. Importantly, Catboost performs data processing in less time and with less memory than other algorithms proposed in the literature. Future research efforts should focus on developing higher performance models and expanding datasets to further improve the accuracy of predictions.
AB - Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the government of Canada has collected a large sensor-based dataset containing detailed information on 7384 light-duty vehicles from 2017 to 2021, with the goal of reducing CO2 emissions by 40–45% by 2030. To this end, various researchers worldwide have developed vehicle emissions and consumption models to comply with these targets and achieve the Canadian government’s ambitious objectives. In this work, we propose the development of boosting and other regression models to predict carbon dioxide emissions for light-duty vehicle designs, with the aim of creating ensemble learning models that leverage vehicle specifications to forecast emissions. Our proposed boosting model is capable of accurately predicting CO2 emissions, even with only one car attribute as input. Moreover, our regression models, in conjunction with the boosting algorithm, can effectively make predictions from various vehicle inputs. Our proposed technique, categorical boosting (Catboost), provides critical insights into transportation-generated air pollution, offering valuable recommendations for both vehicle users and manufacturers. Importantly, Catboost performs data processing in less time and with less memory than other algorithms proposed in the literature. Future research efforts should focus on developing higher performance models and expanding datasets to further improve the accuracy of predictions.
KW - Catboost
KW - boosting algorithm
KW - carbon dioxide emission
KW - light-duty vehicle
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85160602289&partnerID=8YFLogxK
U2 - 10.3390/electronics12102288
DO - 10.3390/electronics12102288
M3 - Article
AN - SCOPUS:85160602289
SN - 2079-9292
VL - 12
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 10
M1 - 2288
ER -