DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/8/2770 |
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| author | Fan Yang Yanan Qiao Wei Wei Xiao Wang Difang Wan Robertas Damaševičius Marcin Woźniak |
| author_facet | Fan Yang Yanan Qiao Wei Wei Xiao Wang Difang Wan Robertas Damaševičius Marcin Woźniak |
| author_sort | Fan Yang |
| collection | DOAJ |
| description | Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation. |
| first_indexed | 2024-03-10T20:25:32Z |
| format | Article |
| id | doaj.art-ca0542e5e58f4a70b07f8d2f40776508 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:25:32Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ca0542e5e58f4a70b07f8d2f407765082023-11-19T21:50:35ZengMDPI AGApplied Sciences2076-34172020-04-01108277010.3390/app10082770DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart NavigationFan Yang0Yanan Qiao1Wei Wei2Xiao Wang3Difang Wan4Robertas Damaševičius5Marcin Woźniak6School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Management, Xi’an Jiaotong University, Xi’an 710049, ChinaDepartment of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, LithuaniaFaculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, PolandTimely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.https://www.mdpi.com/2076-3417/10/8/2770marine navigation safetydepth predictionhybrid modeldeep learningsmart navigation |
| spellingShingle | Fan Yang Yanan Qiao Wei Wei Xiao Wang Difang Wan Robertas Damaševičius Marcin Woźniak DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation Applied Sciences marine navigation safety depth prediction hybrid model deep learning smart navigation |
| title | DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation |
| title_full | DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation |
| title_fullStr | DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation |
| title_full_unstemmed | DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation |
| title_short | DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation |
| title_sort | ddtree a hybrid deep learning model for real time waterway depth prediction and smart navigation |
| topic | marine navigation safety depth prediction hybrid model deep learning smart navigation |
| url | https://www.mdpi.com/2076-3417/10/8/2770 |
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