Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM
Despite the development of advanced technology, marine accidents have not decreased. To prevent marine accidents, it is necessary to predict accidents in advance. With the recent development of artificial intelligence (AI), AI technologies such as deep learning have been applied to create and analyz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11724 |
| _version_ | 1797465990601113600 |
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| author | Jungyeon Choi |
| author_facet | Jungyeon Choi |
| author_sort | Jungyeon Choi |
| collection | DOAJ |
| description | Despite the development of advanced technology, marine accidents have not decreased. To prevent marine accidents, it is necessary to predict accidents in advance. With the recent development of artificial intelligence (AI), AI technologies such as deep learning have been applied to create and analyze predictive models in various fields. The purpose of this study is to develop a model for predicting the frequency of marine accidents using a long-short term memory (LSTM) network. In this study, a prediction model was developed using marine accidents from 1981 to 2019, and the proposed model was evaluated by predicting the accidents in 2020. As a result, we found that marine accidents mainly occurred during the third officer’s duty time, representing that the accidents are highly related to the navigator’s experience. In addition, the proposed LSTM model performed reliably to predict the frequency of marine accidents with a small mean absolute percentage error (best MAPE: 0.059) that outperformed a traditional statistical method (i.e, ARIMA). This study could help us build LSTM structures for marine accident prediction and could be used as primary data to prevent the accidents by predicting the number of marine accidents by the navigator’s watch duty time. |
| first_indexed | 2024-03-09T18:29:28Z |
| format | Article |
| id | doaj.art-8d2008cac9cf4c4bbdc67684cb5e0270 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:28Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8d2008cac9cf4c4bbdc67684cb5e02702023-11-24T07:39:52ZengMDPI AGApplied Sciences2076-34172022-11-0112221172410.3390/app122211724Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTMJungyeon Choi0LINC 3.0 Project Group, Mokpo National Maritime University, Mokpo 58628, Republic of KoreaDespite the development of advanced technology, marine accidents have not decreased. To prevent marine accidents, it is necessary to predict accidents in advance. With the recent development of artificial intelligence (AI), AI technologies such as deep learning have been applied to create and analyze predictive models in various fields. The purpose of this study is to develop a model for predicting the frequency of marine accidents using a long-short term memory (LSTM) network. In this study, a prediction model was developed using marine accidents from 1981 to 2019, and the proposed model was evaluated by predicting the accidents in 2020. As a result, we found that marine accidents mainly occurred during the third officer’s duty time, representing that the accidents are highly related to the navigator’s experience. In addition, the proposed LSTM model performed reliably to predict the frequency of marine accidents with a small mean absolute percentage error (best MAPE: 0.059) that outperformed a traditional statistical method (i.e, ARIMA). This study could help us build LSTM structures for marine accident prediction and could be used as primary data to prevent the accidents by predicting the number of marine accidents by the navigator’s watch duty time.https://www.mdpi.com/2076-3417/12/22/11724marine accidentprediction modeldeep learningLSTMtime series |
| spellingShingle | Jungyeon Choi Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM Applied Sciences marine accident prediction model deep learning LSTM time series |
| title | Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM |
| title_full | Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM |
| title_fullStr | Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM |
| title_full_unstemmed | Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM |
| title_short | Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM |
| title_sort | predicting the frequency of marine accidents by navigators watch duty time in south korea using lstm |
| topic | marine accident prediction model deep learning LSTM time series |
| url | https://www.mdpi.com/2076-3417/12/22/11724 |
| work_keys_str_mv | AT jungyeonchoi predictingthefrequencyofmarineaccidentsbynavigatorswatchdutytimeinsouthkoreausinglstm |