## 3 Methods
### Input Factor Extraction from Remote Sensing Images  
In this study, seven parameters in three categories extracted from remote sensing images were selected as the nodes of the input layer of the ANN model: (1) multi-spectral bands (HJ-1: B1, B2, B3, B4); (2) geographical coordinates (X, Y); and (3) the distance from waterlines (D).  
Remote sensing spectral bands: The intertidal zone can be separated into three distinct zones related to elevation and tidal level. The lower tidal flats lie between the mean low water nap and the mean low water spring tide levels and are often subjected to strong tidal currents, the middle flats are located between the mean low water neaps and the mean high water neaps, and the upper flats lie between the mean high water neaps and the mean high water springs. Different zones have different spectral characteristics. At upper tidal flats, the reflectance in the NIR range (B4: near-infrared, 0.76-0.90 \(\upmu\)m) tends to be higher and the B3 (red, 0.63-0.69 \(\upmu\)m) tends to be lower due to a small amount of vegetation and lower water content. The B1 (blue, 0.43-0.52 \(\upmu\)m) and B2 (green, 0.52-0.60 \(\upmu\)m) can be used to interpret and identify tidal channel and creeks in the lower tidal flats due to relatively high reflectance. However, there is great diversity in the morphology, sediments, bedforms, and ecology of these three zones that relates to the changing balance of physical, sedimentological, and biological forces. These diversities are inevitable reactions through all (HJ-1: four) spectral bands of satellite images. Only dependking on a single band and simple linear statistics cannot establish an accurate association with elevation of tidal flats. Thus, in this study, four bands were all considered as elevation-related elements in the ANN model with nonlinear method. The images are different in different moments in the same tidal flat due to the weather and atmospheric conditions. Thus, the preprocessing of images is required. In this paper, we used geometric correction and histogram matching for this preprocessing. Histogram matching is a mathematical transformation used to eliminate and correct the color and brightness differences between the two images. The histogram matching process is done based on ERDAS 9.1 software (Main, Image Interpreter, Radiometric Enhancement, and Histogram Matching).  
Geographic coordinates: The spatial stability of the tidal flats can be represented by basic geographical coordinates. For the same area, topographic data often change within a certain range, while the high value and low value of elevation remain in a controllable interval. In this study, geographic coordinates were used as the input factors to provide the range of elevation in the simulated tidal flats. All coordinates are located in the WGS_1984_UTM_Zone_51N datum.  
Distance from waterlines: In the Tiaozini tidal flats, tidal creeks like Xidagang, Dongdagang, and Tiaoyugang (Figure 1) play a core role in geometric evolution. These flats are considered as positive topography '+', while the creeks are a type of negative topography '-'. For the flats, the longer the distance from the waterline, the higher the elevation, and vice versa; for the creeks, the longer the distance from the waterline, the lower the elevation, and vice versa. Hence, the distance D is considered an important factor in topographic modelling. Based on the waterline, the area was divided into two categories: flats '+' and creeks '-'. According to each pixel, the D has a positive or a negative value. Secondly, based on the ArcGIS spatial analysis, the shortest distance (D) between each pixel and waterline is calculated. The D factor is a vector feature that contains not only the distance length but also positive and negative attributes.