为了提高潮汐水位的实时预测精度,本文提出了一种基于灰色的数据处理群模块化(Grey-GMDH)潮汐水位实时预测模型。模块化将潮汐分解为两部分:由天体引潮力形成的天文潮部分和由各种天气以及环境因素引起非天文潮部分。使用Grey-GMDH模型和调和分析模型分别对潮汐的非天文潮和天文潮部分进行仿真预测,然后将两部分的预测结果综合形成最终的潮汐预测值。并选用San Diego港口的实测潮汐值数据进行实时预报的仿真实验,实验结果验证了该方法的可行性与有效性并取得了良好的仿真结果,验证了模型有着较高的预报精度。

A novel approach is proposed for real-time tidal level forecasting.Real-time forecasting of tidal level is of great significance for human being activities in the fields of coastal and marine engineering.Nevertheless,the disturbance factors of tidal level are quite complicated which deteriorate the tidal forecasting accuracy.In order to improve the accuracy of real-time tidal level forecast,a modular realtime tidal level prediction approach is proposed based on Grey group method of data handling network.GMDH is a polynomial network which is utilized in forecasting and pattern recognition.However,GMDH is commonly sensitive to non-deterministic time series which may lead to low accuracy of forecasting.In this study,the grey theory is introduced into the GMDH network to reduce the unfavorable effects of uncertainty caused by environmental factors and the adverse effects caused thereby on the forecasting accuracy.The modular approach is divided into two parts:the astronomical tide portion caused by celestial bodies′movement and the non-astronomical tide portion caused by various weather and environmental factors.The Grey-GMDH model is used to predict the non-astronomical tide portion,while the harmonic analysis model is used to predict the astronomical tide portion.Then the final prediction result is achieved by combining the estimation outputs of harmonious analysis model and the Grey-GMDH model.In order to verify the validity and accuracy of the proposed prediction model,the real-measured tidal level data of Port of San Diego is chosen as the test database.Simulation results have demonstrated that the proposed method can give predictions for tidal level in real time with high accuracy and satisfactory stability.