# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
## 5 Conclusions and Future Work  
Depth of the waterway is a critical factor in the safety of marine traffic. However, due to the uncertainty of the waterway depth, it is difficult to obtain accurate waterway depth data in a timely manner. In this paper, we used the waterway depth data available from Global Mapper and the AIS data, together with the proposed DDTree model to predict the waterway depth data. The decision tree model is used to select the optimal attributes, and the AIS data are processed using the DNN. Then the neural network learns to perform inference reasoning on the sufficient waterway depth based on the ship's behavior (the ship did not go around). The experimental results show that the DDTree model has higher accuracy for prediction of waterway depth and can complete prediction of waterway depth more efficiently. Thus, it can compensate for the lack of waterway depth data in marine transportation  
Figure 12: Difference plot between the ground truth depth map and the predicted depth map for Nantong (**a**) and Fangcheng (**b**) ports.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Deep Neural Network** & **Hybrid DDTree Model** \\ \hline MSE & 6.71 & 5.23 \\ \(R^{2}\) & 0.82 & 0.84 \\ \(F_{1}\) & 0.61 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of depth classification: mean square error (MSE) and coefficient of determination (\(R^{2}\)).  
and provide for faster update of waterway depth data. The results for prediction of depth using DDTree had an accuracy rate up to 91.15%.  
However, there are still some disadvantages of prediction of waterway depth through DNN, as the network parameters can influence the prediction ability of DNNs. In future research, firstly, the sources of waterway depth data will be expanded, and the prediction methods will be optimized. Secondly, we hope to get inspiration from [41] to improve the resource utilization of our proposed model and enhance the security and data processing capacity of the prediction of waterway depth. Besides, DNNs and large-scale flow data processing will be considered to optimize network parameters, and a visual module will be introduced to display the map of predicted depth data in real time, which will improve the safety of marine transportation and will provide a reference for the smart navigation of ships.  
Conceptualization, F.Y., Y.Q., W.W., X.W., D.W., and M.W.; formal analysis, Y.Q., and W.W.; investigation, X.W., D.W., and R.D.; methodology, F.Y. and Y.Q.; supervision, Y.Q.; writing--original draft, F.Y.; visualization, F.Y. and R.D.; writing--review and editing, R.D. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National key R&D Program of China under grant number 2018 YFB1402700. This work was also supported by the Key Research and Development Program of Shaanxi Province (number 2018 ZDXM-GY-036), the Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data (number IPED7), the National Natural Science Foundation of China (grant numbers 61761042, 61941112), and the Key Research and Development Program of Yanan (grant numbers 2017 KG-01, 2017 WZZ-04-01).  
The authors declare no conflict of interest.