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DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

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School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
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School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
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School of Management, Xi’an Jiaotong University, Xi’an 710049, China
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Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2770; https://doi.org/10.3390/app10082770
Received: 11 March 2020 / Revised: 6 April 2020 / Accepted: 9 April 2020 / Published: 16 April 2020
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. View Full-Text
Keywords: marine navigation safety; depth prediction; hybrid model; deep learning; smart navigation marine navigation safety; depth prediction; hybrid model; deep learning; smart navigation
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Yang, F.; Qiao, Y.; Wei, W.; Wang, X.; Wan, D.; Damaševičius, R.; Woźniak, M. DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation. Appl. Sci. 2020, 10, 2770.

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