Mapping predicted carbon dioxide emissions from ships using gradient-boosting-based models

1. Introduction

Ship emissions affect the environment, economy, and human health. Environmental damage includes climate change caused by greenhouse gases (GHG) such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) [1][2]. Climate change affects the weather (higher average temperatures, longer-lasting droughts, more intense wildfires, and stronger storms), environ-ment (melting sea ice, sea level rise, flooding, warmer ocean waters and marine heat waves, and ecosystem stressors), agricul-ture (less predictable growing seasons, reduced soil health, and food shortages), animals, and humans (human health, worsening inequity, displacement, and economic impacts) [3]. As CO₂ ac-counts for the largest proportion of greenhouse gas (GHG) emis-sions, research and policies to reduce it are actively underway. Internationally, efforts have focused on regulating GHG emis-sions from ships by introducing the existing energy efficiency ship index (EEXI) and carbon intensity indicator (CII) [4]. Fur-thermore, research is actively being conducted on eco-friendly fuels such as ammonia, methanol, and hydrogen, and post-treatment processes such as CO₂ capture system (carbon capture, utilization, and storage (CCUS)) [5]-[7].

Economic damage includes carbon taxes caused by ship emissions and damage caused by natural disasters due to global warming. Human health damage includes premature deaths ow-ing to global shipping-sourced emissions [8]. Therefore, moni-toring ship emissions and implementing appropriate measures to reduce them is crucial. However, installing equipment to measure ship emissions is not common owing to high equipment costs and less stringent regulations.

To address this issue, studies have focused on predicting emis-sions using machine learning (ML)-based methods. Yang et al. predicted GHG emissions with 92.5% accuracy based on the sailing speed, displacement, and weather conditions in an oil tanker noon report [9]. Lee et al. predicted fuel consumption and carbon emissions using random forest, LightGBM, and long short-term memory (LSTM) based on the operation data of dual-fuel propulsion ships [10]. Cao et al. predicted fuel sulfur content using an ultraviolet image of an exhaust plume obtained from an ultraviolet camera and a convolutional neural network [11]. Reis et al. used regression models based on profile-driven features and statistical pattern analysis to predict the CO₂ emissions of Ro-Pax ships sailing medium and long routes [12]. Liu and Duru developed a Bayesian ship traffic generator to overcome the limi-tation of the inability to identify future ship movements, with ship emission forecasts generated accordingly [13]. Lepore et al. compared and analyzed several regression techniques to predict CO₂ emissions [14]. In addition to focusing on emissions predic-tion, research on emissions mapping is actively underway.

Buber et al. used point density analysis in MapInfo v.16 to represent the spatial distribution of emissions from domestic shipping [15]. Topić estimated the CO₂ emissions of container-ship traffic for a year using statistical energy analysis methodolo-gy and expressed this on a spatial map [16]. Huang et al. ex-pressed the annual NOx emissions from ships as spatial distribu-tions using traditional and dynamic methods [17]. However, no study has predicted or mapped the emissions generated from ships in real time. Therefore, in this study, we created a model to predict emissions using ML technologies based on big data col-lected from ships and then mapped these emissions accordingly. Section 2 describes ship specifications, sailing routes, and data acquisition. Section 3 describes the gradient-boosting-based algo-rithms—extreme gradient boosting (XGBoost) and light gradi-ent-boosting machine (LightGBM). Section 4 describes data preprocessing, model training, and hyperparameter optimization. Section 5 discusses the results of hyperparameter optimization, prediction results, and mapping of the predicted emissions. Sec-tion 6 summarizes the results.

2.Data Acquisition

2.1 Ship Specifications and Sailing Routes

The test object used to predict the emissions in this study was a recently built training ship. The training ship, built in 2019, is equipped with a data acquisition (DAQ) system that automatical-ly collects information from the sensors of various marine ma-chinery. The main specifications of the training ship are listed in Table 1.

Table 1:

Main specifications of the training ship.

The training ship, shaped like a cruise ship, is equipped with an electronic control main engine (M/E) from MAN E-S, with the long shaft installed according to the training ship structure.

The training ship sails periodically to train future navigators and engineers, with the routes on which the data are used in this study described in Table 2.

Table 2:

Description of sailing routes.

The training ships sailed to Masan, Incheon, and Ulleungdo at monthly intervals from April to June 2022. The sailing route for each voyage is illustrated in Figure 1. Global positioning system (GPS) information was collected for each voyage, and the sailing routes were displayed according to latitude and longitude, as shown in Figure 1.

Figure 1:

Sailing routes of Masan, Incheon, and Ulleungdo voy-ages

In Figure 1, blue, purple, and green represent the Ulleungdo, Masan, and Incheon voyages, respectively. All voyages began in Busan, where the training ship was located.

3.Gradient Boosting-Based Algorithms

Gradient boosting is a technique applied to decision tree-based algorithms. This method creates a strong ensemble model by considering the decision tree as a weak learner and continuously training it using gradient descent to minimize the error between the actual and predicted values. In other words, each new deci-sion tree is trained based on the errors of the previous decision tree. Representative algorithms based on gradient boosting in-clude DMLC XGBoost and Microsoft's LightGBM [18][19].

4.Modeling

4.1 Data Preprocessing

The Masan, Incheon, and Ulleungdo datasets were combined to create the total dataset, with a heat map created (Figure 2) to confirm the correlation between the input and output variables for the total dataset. Considering the output variable CO₂ is on the right side of the correlation heatmap, its correlation with the input variables can be verified. Referring to Figure 2, the “M/E scav-enge air receiver temperature” and “average cylinder compression pressure” were removed because they had low correlations with CO₂.

Figure 2:

Correlation heatmap for the total dataset

The total dataset was divided into training, validation, and test-ing sets at a ratio of 6/2/2, with the data randomly shuffled during the division.

4.2 Model training

The XGBoost and LightGBM models were trained on the training set using default hyperparameters. Performance metrics of mean absolute error (MAE), R², and mean absolute percentage error (MAPE) were used to evaluate the performance of the trained models; their equations are listed in Table 3 [27].

Table 3:

Performance metrics for evaluation

The prediction results for the training, validation, and testing sets of the trained models are listed in Table 4. Both models had high-performance metrics in the order of training, validation, and testing sets. When comparing the performances of XGBoost and LightGBM, XGBoost exhibited a higher performance for all datasets.

Table 4:

Performance metrics for XGBoost and LightGBM with default hyperparameters

4.3 Hyperparameter Optimization

XGBoost and LightGBM have various hyperparameters, and their model performance improves depending on their optimal combination. Therefore, this study uses the weight and bias (W&B) library, which performs the hyperparameter optimization process.

Considering XGBoost and LightGBM are gradient-boosting-based algorithms that share similar hyperparameters, hyperpa-rameters common to both algorithms were selected for optimiza-tion. The selected hyperparameters and their ranges are listed in Table 5.

Table 5:

Hyperparameters and their ranges for XGBoost and LightGBM.

“Learning_rate” sets how much previous results are reflected at each learning stage. “N_estimators” or “Num_iterations” is the number of decision trees. “Subsample” is the data sampling rate for each decision tree. “Colsample_bytree” is the feature sam-pling rate for each decision tree. “Max_depth” is the maximum depth of the decision tree. “Min_child_weight” is the minimum sum of weights for all observations required in child.

In the W&B optimization process, the range of hyperparame-ters was set, and optimization was performed 100 times by ran-dom search, aiming to minimize the MAE.

5.Result and Discussion

5.1 Results of Hyperparameter Optimization

Figures 3 and 4 show the MAE performances of XGBoost and LightGBM over time, respectively. The MAE values are inconsistent because the hyperparameters are tuned using a ran-dom search method in the W&B library. Comparing XGBoost and LightGBM, XGBoost performed better for more hyperpa-rameter combinations than LightGBM, suggesting that the XGBoost model exhibits higher overall performance than the LightGBM model for the data and hyperparameter range used in this study.

Figure 3:

Progress of hyperparameter optimization for XGBoost

Figure 4:

Progress of hyperparameter optimization for LightGBM

Figures 5 and 6 show the hyperparameter tuning processes of the two models in parallel coordinate plots. As confirmed in Figures 3 and 4, most of the hyperparameter combinations in Figure 5 show that the MAE converges to nearly zero, and in Figure 6, they appear to have various MAE ranges. Considering the parallel coordinate plot displays the results of hyperparameter tuning in real-time as a colored line, initially setting the hyperpa-rameter ranges would help developers.

Figure 5:

Parallel coordinate plot for XGBoost

Figure 6:

Parallel coordinate plot for LightGBM

Table 6 shows hyperparameter combinations with the lowest MAE for the validation set as a result of hyperparameter tuning. The two models shared the same hyperparameters for “Learn-ing_rate” and “Colsample_bytree, ” and similar hyperparameters for “Subsample” and “Max_depth.” However, “N_estinators” and “Min_child_weight” showed conflicting results.

Table 6:

Optimized hyperparameters for XGBoost and LightGBM.

Figures 7 and 8 show the importance of the parameters in the two models. Parameter importance represents the feature im-portance values for the model by training the model with hyperparameters as inputs and the performance metric as the target output.

Figure 7:

Parameter importance for XGBoost

Figure 8:

Parameter importance for LightGBM

The parameter importance results showed that for both mod-els, “Learning_rate” was the most important for MAE. Further-more, “N_estimators” or “Num_iterations” were also shown to be important for MAE. The “Colsample_bytree” was more im-portant than “Num_iterations” in LightGBM and followed “N_estimators” in XGBoost. Considering our model was trained based on big data, “N_estimators, ” i.e., the number of trees, was important in hyperparameter optimization. In terms of the overall parameter importance of the two models, the order of importance was similar.

5.2 Prediction Results of Optimized XGBoost and LightGBM

Table 7 compares the performances of the training, validation, and testing sets using the two models to which the optimal hy-perparameters obtained in Table 6 were applied. Both models exhibited high performance on the training, validation, and testing sets for all performance metrics.

Table 7:

Performance metrics for XGBoost and LightGBM with optimized hyperparameters

Compared to Table 4, which shows the results of models trained with default hyperparameters, Table 7, with optimized hyperparameters, showed a higher performance for all perfor-mance metrics and datasets, confirmed by referring to the per-centage values of the difference in the performance metrics be-tween Tables 4 and 7. This result shows that hyperparameter optimization is essential for improving model performance.

5.3 Mapping of Predicted Emissions by Optimized XGBoost and LightGBM

A range of CO₂ emissions values was used to map predicted CO₂ emissions. The CO₂ range refers to the minimum (0.1798%) and maximum (6.9169%) CO₂ values and quartiles of 25% (4.62%), 50% (5.07%), and 75% (5.17%) of the CO₂ values in the total dataset.

Figures 9 and 10 show the mapping of CO₂ emissions pre-dicted by XGBoost and LightGBM for the three voyages. The color bar at the top right indicates the range of CO₂ emissions and the highest CO₂ emissions in the order of black, red, orange, and yellow.

Figure 9:

Mapping of CO2 emissions for three voyages by XGBoost

Figure 10:

Mapping of CO2 emissions for three voyages by LightGBM

The Folium library has a scroll function to provide an overall view, as shown in Figures 9 and 10, with the enlarged maps shown in Figures 11 and 12. Figures 11 and 12, which show the CO₂ emissions for the Ulleungdo voyage, indicate that the two models show similar overall predictions. With overall CO₂ emis-sions mostly in red and black, considerable CO₂ emissions oc-curred on this voyage.

Figure 11:

Enlarged CO2 mapping for Ulleungdo voyage by XGBoost

Figure 12:

Enlarged CO2 mapping for Ulleungdo voyage by LightGBM

Figure 13 and 14 present the CO₂ emission mapping for the Masan voyage; while both models show CO₂ emissions with orange and yellow colors, they also show black and red colors of CO₂ emissions at some middle points.

Figure 13:

Enlarged CO2 mapping for Masan voyage by XGBoost

Figure 14:

Enlarged CO2 mapping for Masan voyage by LightGBM

Figures 15 and 16 present the CO₂ emission mapping for the Incheon voyage, indicating most of the CO₂ emissions were orange- and yellow-colored. With this route, emissions were considered reduced because of the complete combustion of the fuel injected into the M/E.

Figure 15:

Enlarged CO2 mapping for Incheon voyage by XGBoost

Figure 16:

Enlarged CO2 mapping for Incheon voyage by LightGBM

6.Conclusion

Considering that CO₂ emissions from ships can cause various Considering that CO₂ emissions from ships can cause various environmental, economic, and human health problems, monitor-ing CO₂ emissions on the ship itself or by a ship management company will become important in the future when eco-friendliness is emphasized. Therefore, we conducted a study to predict CO₂ emissions and mapped them using gradient boost-ing-based models.

Vessel and emission data collected for the Masan, Ulleungdo, and Incheon voyages, each a month apart from April to June 2022, were converted into a total dataset through preprocessing, with the total dataset divided into training, validation, and testing sets in a ratio of 6/2/2.

The XGBoost and LightGBM models were trained using the default hyperparameters, with their performance evaluated using the hyperparameters optimized using the W&B library. As a re-sult, MAE/R²/MAPE for the XGBoost and LightGBM testing sets were 0.1394/0.8388/0.0782 and 0.1782/0.8068/0.0698, respectively, indicating that the performance of the optimized model was superior to that of the models with the default hy-perparameters.

CO₂ emissions were predicted by the optimized models, cate-gorized by quartile range, and expressed in black, red, orange, and yellow, in the order of the highest CO₂ emissions. The mapped CO₂ emissions could be checked using an overall view and an enlarged view, thus allowing the ship itself or the ship management company to monitor its CO₂ emissions.

Acknowledgments

This research was supported by the Autonomous Ship Technology Development Program [20016140] funded by the Ministry of Trade, Industry, & Energy (MOTIE, Korea); and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [NRF-2022R1F1A1073764]; and the Korea Maritime & Ocean University Research Fund in 2023.

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