## 1 Introduction
In addition, the area of tidal flats should be small and stable so that the waterlines can be identified as contour lines with the same tidal level [17]. Above all, the previous studies mostly manipulated the waterline method in stable areas such as bay or gulf, with relatively homogeneous coastal environments and the vertical error was within 0.5 m [15]. However, caused by tides, waves, and tidal channel migration, tidal flats are usually faced with high morphological changes [18]. Just like a large sandbank Tiaozini in RSRs, Jiangsu coast, China, significant temporal and spatial morphological changes occur in tidal flats due to the continual migration of tidal creeks, which can reach several kilometers in two or three months [3]. According to those variable areas, whether the waterline method can be used to monitoring the topography timely and without interruption needs to be further explored. Our study attempted to apply the waterline method to an area with high morphological changes and broader regions. To solve this problem, in this study, single images under a low tidal level that can present instantaneous geomorphic distribution are used to construct the DEM. This can help to increase monitoring frequency, and is more suitable to changeable areas. Thus, except for water-edges, we sought to excavate more information (spectral bands, geographic type, and position and distance) from single remote sensing images of tidal flats. These data are used to build a relationship model with measured elevation data. Due to a lack of measured data, the elevation data was collected based on the topography map results in the literature [13]. Another problem, however, in dealing with elevation data in the context of prediction model development lying in their inherently fuzzy and non-linear relationships with remote sensing data [19]. To overcome this obstacle, we used an artificial neural network (ANN) as our modelling framework. ANN imitates the information transfer process used by human beings and is applicable to solving complex and nonlinear problems [20]. There are many types of learning algorithms used to train ANNs, and the back propagation (BP) algorithm is one of the most commonly used techniques in the field [21]. It has the advantages of a nonlinear, sample-based, free model structure [22], and has been used to correlate large and complex datasets without any prior knowledge of the relationships between them in many disciplines, such as in water depth retrieval [23; 24; 25]. However, owing to shallow water restrictions or high water turbidity, few studies have applied a BP ANN model to simulate the topography of large-scale tidal flats. The objective of this study was to construct a DEM of an intertidal zone with high geomorphic evolution. Based on the resulting map of the waterline method, an ANN model using a suite of geomorphological factors from a single remote sensing image was developed and evaluated. Taking the Tiaozini tidal flats in the Central Jiangsu coast as the study area, with the support of remote sensing (RS), geographic information system (GIS), and neural network simulation technologies, DEMs for remote sensing images taken at other times were obtained. The accuracy and applicable conditions for constructing DEMs of tidal flats were also be discussed.