Mapping predicted carbon dioxide emissions from ships using gradient-boosting-based models
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초록
Monitoring the amount of CO2 emitted by individual ships in real-time is currently impossible. Therefore, we conducted a study to predict and map CO2 emissions using gradient boosting-based models trained based on ship data and emission data collected from ships. The extreme gradient boosting (XGBoost) and light gradient-boosting machine (LightGBM) models were used, and a hyperparameter tuning process was performed to improve the model performance. The results confirmed that hyperparameter optimi-zation improves model performance. Based on these models, CO2 emissions were predicted and expressed in a map with black, red, orange, and yellow colors, according to the range of CO2 emissions for the three voyages, consequently confirming that the ship itself or the ship management company can monitor CO2 emissions from ships by mapping them on a global map.
Abstract
Monitoring the amount of CO2 emitted by individual ships in real-time is currently impossible. Therefore, we conducted a study to predict and map CO2 emissions using gradient boosting-based models trained based on ship data and emission data collected from ships. The extreme gradient boosting (XGBoost) and light gradient-boosting machine (LightGBM) models were used, and a hyperparameter tuning process was performed to improve the model performance. The results confirmed that hyperparameter optimi-zation improves model performance. Based on these models, CO2 emissions were predicted and expressed in a map with black, red, orange, and yellow colors, according to the range of CO2 emissions for the three voyages, consequently confirming that the ship itself or the ship management company can monitor CO2 emissions from ships by mapping them on a global map.
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