# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
###### 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. marine navigation safety; depth prediction; hybrid model; deep learning; smart navigation 1  
Focusing & Design &  
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]. Obtaining actual water depths, however, is difficult because the water depth changes constantly. Water depth data that are found in the electronic chart display and information system (ECDIS) and water-way area maps, therefore, 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. For example, in the period from 2013 to 2014, the reef accidents in the Yangtze River navigation channel accounted for 10% of all accidents in the Yangtze River, and the proportion of all kinds of vessel accidents on the Yangtze River was 17% [5]. The total number of accidents caused by insufficient navigation depth was 27%. Therefore, having accurate and timely knowledge of water depth data is of great significance to safe navigation. The problem is especially relevant in the context of Internet-of-Things (IoT) solutions for smart ships [6], connected ships [7], and next generation marine data networks [8] in which the problems of quality of service and environmental awareness arise [9]. In water transport, the water depth of a channel refers to the vertical distance from the water level to the bed of a waterway channel. For a local section of a waterway, it usually refers to the vertical distance from the water level to the bed at the shallowest part of a channel. Safe water depth and the depth of water needed for a ship to pass safely acts as a guide for route planning and safe navigation of ships. The bottom of the shallow channel can be heavily silted under the hydrological conditions of wind, waves, and currents. A review of the literature reveals two broad approaches for obtaining the water depth of a waterway: (1) monitoring of the water depth with sonar and other detection devices, and (2) time-delay acquisition and processing of underwater acoustic signals to calculate the waterway depth. At present, the prediction methods in ship transportation focus on short-term traffic flow prediction, and traffic congestion forecasting.