Mega-nourishment morphology evolution prediction combining spatial and temporal features

To protect coastal areas and ecology environment, Sand nourishment is widely used as an effective method. The morphology evolution and prediction is needed for sand nourishment and maintenance. Based on the observations of a mega-nourishment, a data-driven approach combining temporal and spatial features for the prediction of nourishment morphology evolution is proposed in this paper. The sequence grids from the survey data are used to extract the spatial features of nourishment beach by CNN, which can reduce the complexity of the network and extract the spatial features. And then LSTM is employed to derive the temporal relationship from past time-series features for predicting future nourishment terrain. Finally, the final prediction result is obtained through decoding the LSTM output by the fully connected layer. The complex spatiotemporal correlations of the sand nourishment evolution can be derived by training the proposed model effectively. The proposed model achieved the best performance for nourishment morphology evolution prediction compared with other methods as shown by the experimental results.