Abstract
Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.
Highlights
Tides are periodic fluctuations of seawater generated by the combined gravitational forces of the Moon and Sun and by the inertial centrifugal force required for the relative motion of the Earth
The results show that the proposed HANARX model has robustness as well as high accuracy, and the majority of error values and the differences between observed and predicted tide levels fall within the range of −6.1 cm and +6.7 cm
The prediction accuracy is improved by 20% to 40% compared with the traditional method, the prediction data has high correlation with and low dispersion relative to observation data, the error is stable, and the effect is more prominent under extreme weather conditions
Summary
To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). INDEX TERMS GA-BP neural network, Harmonic analysis, WNN, Modular prediction, NARX neural network, Tide prediction
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