## 5 Discussion  
Taking Tiaozini as an example, a combined method (waterline method and ANN model) was designed to estimate the elevation of changeable tidal flats. The waterline method was elaborated in detail according to the published literature [13] and applied to support the initial training data for the ANN model. A feed-forward neural network using the back-propagation training algorithm was constructed to simulate the elevation of tidal flats in this study. On the one hand, from the perspective of input parameters, different combinations that affect the accuracy of DEM results were discussed. On the other hand, based on previous studies, a discussion was carried out in terms of vertical accuracy, horizontal accuracy, and time accuracy of the DEM results in different tidal flats sites. According to this ANN model, input parameters are crucial for simulation accuracy. In this study, three different combinations of input parameters were discussed. The first one is only four input nodes (B1, B2, B3, B4), and the network structure of '4-15-15-1'. The second one is six input nodes (B1, B2, B3, B4, X, Y), and the network structure of '6-15-15-1'. The last one is seven input nodes (B1, B2, B3, B4, X, Y, D), and the network structure of '7-15-15-1'. We used the same input data from the image (21 March 2014) to simulate the topography and compared the accuracy of three ANN models with different network structures. The seven-parameter group had the smallest error compared to the six-parameter and four-parameter groups. The RMSE can reach 0.32 m, and the R\({}^{2}\) can reach 0.90 (Table 2). Figure 9 shows the distribution of the error percentage. Only 60% of the error value of the neural network model based on four parameters was within \(\pm\) 1 m, 40% was within \(\pm\) 0.5 m, and 15% was within \(\pm\) 0.2 m. With the addition of parameters, the accuracy improved rapidly. The error value of the six-parameter model was 85% within \(\pm\) 1 m, 60% within \(\pm\) 0.5 m, and 25% within \(\pm\) 0.2 m. The error value of the seven-parameter model was 94% within \(\pm\) 1 m, 72% within \(\pm\) 0.5 m, and 51% within \(\pm\) 0.2 m. This result shows that parameter D contributed the most to improving accuracy. In addition, each tidal Creek system occupies a \"drainage basin\" on the tidal flat [3]. The topographical changes are largely related to the tidal creeks, which migrate frequently [29]. During the tide rising, the water flows along the tidal creeks to the flat's surface. When two water flows from adjacent creeks meet each other at the middle flats, the velocity decreases, and the sand is deposited in this confluence zone. Thus, the elevation is lower near the tidal creeks and higher in the tidal flats and sand ridges. Toward the center of the flats, the elevation value gradually increases with distance, and toward the center of the tidal creeks, the elevation value gradually decreases with distance. Parameter D reveals the basic layout of the topography. In addition, the nonlinear ANN model's free structure allows us to consider the nonlinear relationships between multiple parameters and elevations, thus allowing more accurate estimations to be obtained. Since the ANN models (a black box model) do not have to consider the factors affecting the reflective properties of water columns and do not have to be trained by newer in-situ measurements, this methodology can be practically applied without handling any complex reflectance separation process and can also be reliably used for repetitive bathymetric mapping, provided that there is an available representative input dataset. Figure 9: Comparison chart of the error value distribution of different parameter combination models. The waterline method is the only current useful approach in the practical application of satellite remote sensing to tidal flat environments [10; 12]. It has been successfully applied to many intertidal flat zones worldwide according to precious studies.