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All Journal JOIV : International Journal on Informatics Visualization Journal of Emerging Science and Engineering
Pham, Nguyen Dang Khoa
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Computer Science & IT Engineering Environmental Science

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Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices Nguyen, Hoang Phuong; Nguyen, Cao Thao Uyen; Tran, Thi Men; Dang, Quoc Hai; Pham, Nguyen Dang Khoa
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2581

Abstract

This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase capability. Throughout this work, the importance of the role that artificial intelligence plays in driving efficiency, safety, and sustainability is emphasized. This work also highlights the urgent need for action to address climate change and environmental degradation in the marine sector. The marine industry can lessen its carbon footprint, decrease pollution, and improve ecosystem health if it shifts to various alternative fuels, renewable energy sources, and technologies powered by artificial intelligence. At the end of this work, an appeal is made to policymakers, industry stakeholders, and technology providers, urging them to prioritize investments in artificial intelligence research and development and to create collaboration to speed up the transition to a marine sector that is more sustainable and resilient.
Analysis of weather and ship-type effects on fuel efficiency and emissions for green maritime operations Le, Ngoc Doanh; Pham, Nguyen Dang Khoa
Journal of Emerging Science and Engineering Vol. 3 No. 2 (2025)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2025.e58

Abstract

This research is an endeavour to develop an explainable machine learning framework to quantify the combined effect of ship type, fuel type, distance and weather conditions affecting fuel consumption and CO2 emissions for green maritime operations. The data collected from the voyage-level records for various vessel categories was pre-processed and used to train three supervised regression models: Linear Regression, Random Forest, and Extreme Gradient Boosting (XGBoost). The models were tested based on the coefficient of determination (R2) and mean squared error for training and test data sets separately for fuel consumption and CO2 emission. Results show that all models are able to capture the main trends, but the Random Forest was able to provide the most accurate and robust predictions, with values of test R2 exceeding 0.94 and the lowest values of error for both target variables. In order to improve the interpretability, SHapley Additive exPlanations (SHAP) analysis and feature importance measures were used for the Random Forest models. Distance becomes the main factor, whereas ship type, fuel type, and weather variables have a secondary but significant impact on fuel consumption and emissions. The proposed approach offers a transparent and computationally efficient aid for supporting operational optimization, fuel choice evaluation, and policy design in the context of maritime decarbonization.
Co-Authors Dang, Quoc Hai Le, Ngoc Doanh Nguyen, Cao Thao Uyen Nguyen, Hoang Phuong Tran, Thi Men