An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Van Vien Vu - Faculty of Tourism, Ha Long University, Quang Ninh, 200000, Vietnam
Phuoc Tai Le - Postgraduate Institute, Ho Chi Minh City University of Transport, Ho Chi Minh, 700000, Vietnam
Thi Mai Thom Do - Faculty of Financial Management, Vietnam Maritime University, Haiphong, 181810, Vietnam
Thi Thuy Hieu Nguyen - Academy of Politics Region II, Ho Chi Minh, 700000, Vietnam
Nguyen Bao Minh Tran - Academy of Politics Region II, Ho Chi Minh, 700000, Vietnam
Prabhu Paramasivam - Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, 602105, India
Thi Thai Le - School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Vietnam
Huu Cuong Le - Institute of Maritime, Ho Chi Minh City, University of Transport, Ho Chi Minh, Vietnam
Thanh Hieu Chau - Institute of Maritime, Ho Chi Minh City, University of Transport, Ho Chi Minh, Vietnam

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.


Machine learning; sustainability; marine transport; marine logistics; Tweedie; random forest

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