Algorithmic Modeling for Predicting Carbon Emissions in an Individual Vehicles: A Machine Learning and Deep Learning Approach


  • Rashmi B. Kale, Nuzhat Faiz Shaikh


Machine Learning, Deep Learning, Air Quality Index, Green House Gases, CariQ, Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, R-squared Score


This study proposes an algorithmic model aimed to accurately predicting carbon emissions from individual vehicles by leveraging machine learning and deep learning techniques. Concerns regarding environmental sustainability and climate change have intensified the need for precise assessments of carbon footprints, particularly in the transportation sector. Traditional methods often lack the adaptability and scalability required to handle the complexity of emission prediction tasks. In contrast, machine learning and deep learning offers promising avenues for developing robust models capable of learning from vast datasets and capturing intricate patterns in vehicle emissions. The purpose of research is to address the breach by designing a deep learning algorithmic framework that integrates machine learning algorithms to analyze real time datasets with vehicle attributes, driving patterns, and fuel characteristics to predict carbon emissions. The proposed approach holds potential for enhancing our understanding of vehicle emissions dynamics and facilitating the development of targeted interventions to mitigate environmental impacts.


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How to Cite

Rashmi B. Kale, Nuzhat Faiz Shaikh. (2024). Algorithmic Modeling for Predicting Carbon Emissions in an Individual Vehicles: A Machine Learning and Deep Learning Approach . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 900–906. Retrieved from



Research Article