Abstract
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.
Highlights
Water transport is the main transportation mode for the ever-increasing volume of international trade
In order to ensure that the ship passes safely through the waters, the water depth should be greater than the sum of the draft of the ship and surplus under keel clearance (UKC) [3]
After training datasets and verification datasets are divided, the network structure and thethe depth datadata are predicted by theby combination of a neural network structureisisadjusted adjusted and depth are predicted the combination of anetwork neural and a decision tree
Summary
Water transport is the main transportation mode for the ever-increasing volume of international trade. Sci. 2020, 10, 2770 the lack of real-time monitoring means, there are often economic losses due to excessive ship loading, resulting in ship stranding and port obstruction In this context, the development of new methods for increasing situational awareness and achieving smart maritime navigation is highly relevant [4]. Water depth data that are found in the electronic chart display and information system (ECDIS) and water-way area maps, are not accurate, which may cause ships to be stranded, collide, and hit rocks Such accidents can result in serious casualties and property loss, and may even cause serious damage to regional transportation. A hybrid decision tree-deep neural network (DDTree) model is constructed to predict safe waterway depths for ships to increase the precision and timeliness of the waterway depth data and provide references for safe marine navigation and route selection.
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