# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
## 2 Related Work  
In the field of waterway transportation, current research related to the prediction of water traffic focuses on progressive water prediction of depth. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate real-time waterway depths, but these data include no direct channel depth information [11]. The large volume ship AIS data are widely used in navigation. In addition, the wide application of GNSSs (global navigation satellite systems) also provides technical support and a safety guarantee for navigation. Therefore, by employing the data-mining techniques on big data from AIS and GNSS, the waterway depth can be predicted. For example, [PERSON] et al. [12] proposed using multi-spectral satellite images to predict water depth change along a waterway. In order to enhance the accuracy of water prediction of depth, [PERSON] et al. proposed a differential dynamic positioning algorithm based on GPS/Baidou which realizes middle precision based on low cost equipment [13]. [PERSON] analyzed the accuracy of static GNSS measurements according to the number of visible satellites, based on different minimal elevation cutoff angles [14]. [PERSON] et al. proposed two synthetic models for sound-speed profiles, one linear and the other bilinear [15]. Similarly, [PERSON] [16] analyzed critical issues in GPS precise positioning. [PERSON] et al. addressed the problem of estimating the measurements' noise statistics of the Global Positioning System (GPS) using the autocovariance least squares technique [17]. [PERSON] et al. [18] proposed a method for predicting the depth of water in a rough composite channel. [PERSON] et al. [19] proposed a wavelet and a fuzzy neural network model to estimate the depth of groundwater. Prediction of waterway depth can be a part of larger models focused on avoiding marine traffic accidents and ship collision. For example, [PERSON] et al. [20] applied short-time space-time analysis of the AIS data to analyze in-land water ship collisions. [PERSON] et al. [21] proposed a ship accident prediction and judgment model based on the AIS data. [PERSON] et al. [22] proposed a method to improve the AIS data collection equipment. Through the detailed analysis of the data received by the AIS receiver in Skynet 3's debugging stage, receiving capability was improved. [PERSON] [23] proposed a Bayesian network-based model for assessing the frequency of ship sinking accidents. Similarly, [PERSON] et al. [24] used generalized Bayesian inference and the Dempster's rule to recognize the patterns of ship traffic from historical AIS data. [PERSON] et al. [25] predicted ship squat behavior in shallow water depending upon waterway depth. They used the group method of data handling (GMDH) neural networks and an adaptive network-based fuzzy inference system (ANFIS) with good results. [PERSON] et al. [26] used the feed-forward neural network (FFNN) and support vector machine (SVM) classifiers to infer ship location from acoustical data in a deep waterway channel. [PERSON] et al. [27] suggested an approach for bathymetric mapping of shallow water of Arabian Gulf near Bahrain using the kriging procedure. [PERSON] and [PERSON] [28] proposed the ship traffic extraction neural network (STENet) to forecast the maritime traffic of the caution area, which represented an increased risk for ships, due to things such as berth limitations. [PERSON] et al. [29] performed multi-criteria route planning using the AIS data while considering waterway depth (in the navigable area) as one of risk factors and criteria for optimization. [PERSON] et al. [30] adopted the kernel density estimation-based method for mining ships' behaviors from the AIS data. [PERSON] et al. [31] proposed a ship traffic simulation model for in the Xiazhimen waterway for analyzing the statistical characteristics of ship traffic. [PERSON] et al. [32] analyzed the spatial-temporal dynamic of traffic in port waterways while using the AIS data. Finally, [PERSON] et al.