Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning
In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/8/871 |
| _version_ | 1827684981517844480 |
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| author | Yongpeng Wang Daisuke Watanabe Enna Hirata Shigeki Toriumi |
| author_facet | Yongpeng Wang Daisuke Watanabe Enna Hirata Shigeki Toriumi |
| author_sort | Yongpeng Wang |
| collection | DOAJ |
| description | In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way. |
| first_indexed | 2024-03-10T08:40:40Z |
| format | Article |
| id | doaj.art-149a747e6b074916afa9d10e36f73ee2 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T08:40:40Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-149a747e6b074916afa9d10e36f73ee22023-11-22T08:15:34ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-08-019887110.3390/jmse9080871Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep LearningYongpeng Wang0Daisuke Watanabe1Enna Hirata2Shigeki Toriumi3Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology, Tokyo 135-8533, JapanDepartment of Logistics and Information Engineering, Tokyo University of Marine Science and Technology, Tokyo 135-8533, JapanCenter for Mathematical and Data Sciences, Kobe University, Kobe 657-8501, JapanDepartment of Information and System Engineering, Chuo University, Tokyo 112-8551, JapanIn this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way.https://www.mdpi.com/2077-1312/9/8/871vessel trajectory predictionLSTMcubic spline interpolationLNGCO<sub>2</sub> emissions |
| spellingShingle | Yongpeng Wang Daisuke Watanabe Enna Hirata Shigeki Toriumi Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning Journal of Marine Science and Engineering vessel trajectory prediction LSTM cubic spline interpolation LNG CO<sub>2</sub> emissions |
| title | Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning |
| title_full | Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning |
| title_fullStr | Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning |
| title_full_unstemmed | Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning |
| title_short | Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning |
| title_sort | real time management of vessel carbon dioxide emissions based on automatic identification system database using deep learning |
| topic | vessel trajectory prediction LSTM cubic spline interpolation LNG CO<sub>2</sub> emissions |
| url | https://www.mdpi.com/2077-1312/9/8/871 |
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