Tidal prediction is of great significance to the production and practice of coastal areas. In order to improve the accuracy of tidal level prediction, a nonlinear exogenous autoregressive network tidal prediction model (DWT-NARX) based on wavelet analysis is proposed. The model decomposes the tidal data by wavelet and realizes hierarchical prediction by NARX neural network. The tidal prediction simulation experiment is carried out by using the measured tidal data of Savannah Port in the United States. The prediction results are compared with the modular NARX tidal prediction (HANARX) model. The results show that the MAE, MSE and RMSE of the prediction results of this method are the smallest. The results show that the accuracy and stability of this method are better than the modular prediction of NARX.
The prediction of port freight volume is of great significance to transportation and port planning. Based on the characteristics of grey GM (1,1) model and RBF neural network model, a combined forecasting model based on grey GM (1,1) model and RBF neural network model is constructed, and the port freight volume is predicted by field survey data. Experiments show that grey RBF neural network can improve the forecasting accuracy, which is effective and feasible for freight volume forecasting.
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