The invention relates to a tide predicting method for the tide is influenced by various factors, including cyclical factors, such as tidal generation force, and non-cyclical factors, such as
wind power,
atmospheric pressure, coast characteristics, rainfall, dip angles of the
lunar orbit and the like. The predicting accuracy of the traditional
harmonic analysis method is influenced by partial tide number, and the traditional
harmonic analysis method cannot analyze the influence of non-cyclical factors; the
artificial neural network method developed recent years overcomes the defect that the non-cyclical factors cannot be predicted by the
harmonic analysis method to a certain extent, but has great data volume required by study training samples and wide involve range, can cover various possible conditions, but has less
station historical data of non-cyclical factors. The invention provides a predict model, wherein factors which influence tide non-cyclically, such as wind directions, rainfall,
storm surge, coast characteristics and the like, can be fused into the model, and
small sample data can receive more accurate results. In the method, a
support vector machine (SVM)-based predict model is established, wherein, an SVM
toolbox is imported into
MATLAB 7.8; training sample data is trained by utilizing svmtrain function; the formed model is tested by using a
test sample svmpredict function; and the trained and tested data can predict the tide in the same tide
test station.