Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model
Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II...
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MDPI AG
2025-05-01
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| author | Menghao Ji Chengyi Zhao |
| author_facet | Menghao Ji Chengyi Zhao |
| author_sort | Menghao Ji |
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| description | Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. |
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| language | English |
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| spelling | doaj-art-88794e2ee28b454bb9d12f62c3ea47d62025-08-20T01:49:28ZengMDPI AGRemote Sensing2072-42922025-05-01179163610.3390/rs17091636Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost ModelMenghao Ji0Chengyi Zhao1School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaAccurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters.https://www.mdpi.com/2072-4292/17/9/1636green tideGeostationary Ocean Color Imager-II (GOCI-II)Extreme Gradient Boosting (XGBoost)OpenDrift |
| spellingShingle | Menghao Ji Chengyi Zhao Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model Remote Sensing green tide Geostationary Ocean Color Imager-II (GOCI-II) Extreme Gradient Boosting (XGBoost) OpenDrift |
| title | Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model |
| title_full | Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model |
| title_fullStr | Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model |
| title_full_unstemmed | Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model |
| title_short | Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model |
| title_sort | significant improvement in short term green tide transport predictions using the xgboost model |
| topic | green tide Geostationary Ocean Color Imager-II (GOCI-II) Extreme Gradient Boosting (XGBoost) OpenDrift |
| url | https://www.mdpi.com/2072-4292/17/9/1636 |
| work_keys_str_mv | AT menghaoji significantimprovementinshorttermgreentidetransportpredictionsusingthexgboostmodel AT chengyizhao significantimprovementinshorttermgreentidetransportpredictionsusingthexgboostmodel |