Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation
This study presents a novel approach to predicting ship speed based on real-time voyage observation data, aiming to enhance maritime safety and operational efficiency. Observational data from a 20,000-ton bulk carrier, including variables such as latitude, longitude, GPS orientation, wind direction,...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1011 |
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| author | Weidong Gan Dianguang Ma Yu Duan |
| author_facet | Weidong Gan Dianguang Ma Yu Duan |
| author_sort | Weidong Gan |
| collection | DOAJ |
| description | This study presents a novel approach to predicting ship speed based on real-time voyage observation data, aiming to enhance maritime safety and operational efficiency. Observational data from a 20,000-ton bulk carrier, including variables such as latitude, longitude, GPS orientation, wind direction, wind speed, and main engine parameters, were collected and preprocessed to mitigate noise and handle missing values. Six machine learning models—the Backpropagation (BP) Neural Network, Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), XGBoost, and LightGBM—were employed to develop predictive models. Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R<sup>2</sup>) of 0.978. The results highlight the potential of the LightGBM model in optimizing ship navigation and improving maritime operational efficiency. These findings offer a reliable foundation for further advancements in predictive maritime technologies and route optimization. |
| format | Article |
| id | doaj-art-587de14bb53c44dfaf71750bc3d242c8 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-587de14bb53c44dfaf71750bc3d242c82025-08-20T02:21:13ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136101110.3390/jmse13061011Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard ObservationWeidong Gan0Dianguang Ma1Yu Duan2Tianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin 300456, ChinaTianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin 300456, ChinaTianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin 300456, ChinaThis study presents a novel approach to predicting ship speed based on real-time voyage observation data, aiming to enhance maritime safety and operational efficiency. Observational data from a 20,000-ton bulk carrier, including variables such as latitude, longitude, GPS orientation, wind direction, wind speed, and main engine parameters, were collected and preprocessed to mitigate noise and handle missing values. Six machine learning models—the Backpropagation (BP) Neural Network, Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), XGBoost, and LightGBM—were employed to develop predictive models. Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R<sup>2</sup>) of 0.978. The results highlight the potential of the LightGBM model in optimizing ship navigation and improving maritime operational efficiency. These findings offer a reliable foundation for further advancements in predictive maritime technologies and route optimization.https://www.mdpi.com/2077-1312/13/6/1011ship speed predictionshipboard observationmachine learningmaritime efficiency |
| spellingShingle | Weidong Gan Dianguang Ma Yu Duan Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation Journal of Marine Science and Engineering ship speed prediction shipboard observation machine learning maritime efficiency |
| title | Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation |
| title_full | Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation |
| title_fullStr | Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation |
| title_full_unstemmed | Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation |
| title_short | Comparisons of Machine Learning Methods in Ship Speed Prediction Based on Shipboard Observation |
| title_sort | comparisons of machine learning methods in ship speed prediction based on shipboard observation |
| topic | ship speed prediction shipboard observation machine learning maritime efficiency |
| url | https://www.mdpi.com/2077-1312/13/6/1011 |
| work_keys_str_mv | AT weidonggan comparisonsofmachinelearningmethodsinshipspeedpredictionbasedonshipboardobservation AT dianguangma comparisonsofmachinelearningmethodsinshipspeedpredictionbasedonshipboardobservation AT yuduan comparisonsofmachinelearningmethodsinshipspeedpredictionbasedonshipboardobservation |