Comparison of Machine Learning Algorithms For Predicting CO2Emissions in the maritime domain

The maritime industry is a significant source of global carbon dioxide (CO 2 ) emissions, and the accurate prediction of emissions in this domain is of paramount importance. In this paper, we conduct a comparative analysis of machine learning algorithms for predicting CO 2 emissions in the maritime...

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Bibliographic Details
Published in2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA) pp. 1 - 4
Main Authors Michalakopoulos, Vasilis, Ilias, Loukas, Kapsalis, Panagiotis, Mouzakitis, Spiros, Askounis, Dimitris
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.07.2023
Subjects
Artificial Intelligence
Carbon dioxide
CO2 emissions
Data integration
Industries
Machine learning
Machine learning algorithms
Maritime Transportation
Model bench-marking
Performance evaluation
Prediction algorithms
Ships
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Summary:The maritime industry is a significant source of global carbon dioxide (CO 2 ) emissions, and the accurate prediction of emissions in this domain is of paramount importance. In this paper, we conduct a comparative analysis of machine learning algorithms for predicting CO 2 emissions in the maritime domain. Using a unique dataset, comprising historical maritime data for different vessel voyages, including vessel type, voyage information and environmental factors, we employ decision trees, a number of regression, and artificial neural network algorithms. Performance evaluation is conducted using established metrics such as R2 (%), Mean Absolute Error, Normalized Root Mean Squared Error and Symmetric Mean Biased Error. Our findings reveal that the Extra Trees Regressor and Multi-layer Perceptron regressor algorithms outperform the other methods in terms of prediction accuracy demonstrating the least amount of error. Our research contributes to the existing literature by highlighting the potential of machine learning in predicting maritime CO 2 emissions and provides insights for further research, such as exploring alternative algorithms and incorporating real-time data Integration.
DOI:10.1109/IISA59645.2023.10345936