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Based on the XGBoost model: prediction of fuel consumption of underway ships and analysis of influencing factors
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Shengdai CHANG1, Yonggang SUN1, Chun YU2
Navigation of China | 2025, 48(4) : 176 - 182
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Navigation of China | 2025, 48(4): 176-182
• Green Shipping •
Based on the XGBoost model: prediction of fuel consumption of underway ships and analysis of influencing factors
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Shengdai CHANG1, Yonggang SUN1, Chun YU2
Affiliations
  • 1.Science & Technology Innovation and Test Center, China Classification Society, 100007, Beijing
  • 2.Technology & Information Department, China Classification Society, 100007, Beijing
Published: 2025-12-25 doi: 10.3969/j.issn.1000-4653.2025.04.020
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To accurately predict the fuel consumption of in service ships, analyze the complex and variable influencing factors of fuel consumption, and quantify their respective impacts, this study selects tankers and bulk carriers for operational data collection and preprocessing. A fuel consumption prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is established, and factor importance is evaluated using the Gain method. The results demonstrate that the proposed model achieves strong computational and predictive performance, with mean absolute percentage errors of 4.88% and 3.92% for the tanker and bulk carrier models, respectively. Among internal factors, ship speed shows the greatest influence, with weights of 0.671 and 0.429 for the two vessel types. Regarding external factors, navigation environment conditions such as wind and waves also exhibit significant impacts.

ship fuel consumption influencing factors  /  fuel consumption prediction  /  XGBoost
Shengdai CHANG, Yonggang SUN, Chun YU. Based on the XGBoost model: prediction of fuel consumption of underway ships and analysis of influencing factors[J]. Navigation of China, 2025 , 48 (4) : 176 -182 . DOI: 10.3969/j.issn.1000-4653.2025.04.020
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Year 2025 volume 48 Issue 4
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Article Info
doi: 10.3969/j.issn.1000-4653.2025.04.020
  • Receive Date：2024-05-24
  • Online Date：2026-03-17
  • Published：2025-12-25
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  • Received：2024-05-24
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Affiliations
    1.Science & Technology Innovation and Test Center, China Classification Society, 100007, Beijing
    2.Technology & Information Department, China Classification Society, 100007, Beijing
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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