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Journal of Emerging Science and Engineering
ISSN : 30260817     EISSN : 30260183     DOI : https://doi.org/10.61435/jese.xxx.xxx
Core Subject : Social, Engineering,
Journal of Emerging Science and Engineering (JESE) is peer-reviewed, and it is devoted to a wide range of subfields in the engineering sciences. JESE publishes two issues of rigorous and original contributions in the Science and Engineering disciplines such as Biological Sciences, Chemistry, Earth Sciences, and Physics, Chemical, Civil, Computer Science and Engineering, Electrical, Mechanical, Petroleum , and Systems Engineering.. JESE publishes original research papers, reviews, short communications, expository articles, and reports. Manuscripts must be submitted in the English language and authors must ensure that the article has not been published or submitted for publication elsewhere in any format, and that there are no ethical concerns with the contents or data collection. The authors warrant that the information submitted is not redundant and respects general guidelines of ethics in publishing. All papers are evaluated by at least two international referees, who are known scholars in their fields. We encourage and request all academics and practitioners in the field of science and engineering to send their valuable works and participate in this journal.
Articles 31 Documents
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.

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