GUI based modular support vector machine tide forecasting method

Abstract

The invention discloses a GUI based modular support vector machine tide forecasting method, which comprises the steps of S1, acquiring continuous-sequence tide level information monitored by a tide station and a tide value forecast by using a harmonic analysis method; S2, making a difference between a measured value acquired by the tide station and the tide value forecast by the harmonic analysis method, acquiring a time sequence of non-astronomical tide, carrying out data accumulation processing on the inputted tide level information and the tide value according to a grey model AGO algorithm, and enabling the processed data to act as input so as to be applied to regression forecasting of a support vector machine; S3, forecasting tide through the support vector machine according to tide forecasting time information set in the step S1; and S4, completing data recovery of a forecast result of the support vector machine through IAGO reverse accumulation processing, wherein the recovered data is used for modifying the tide forecasting value of the harmonic analysis method.

Description

technical field [0001] The invention relates to the field of tide prediction, in particular to a GUI-based modular support vector machine tide prediction method. Background technique [0002] Since the tide is affected by many factors, periodic factors such as tidal force, non-periodic factors such as wind force, air pressure, coastal characteristics, precipitation, the inclination angle of the lunar orbit and so on. The traditional harmonic analysis method calculates the parameters of each tidal component in the model through the statistics and analysis of long-term tidal data, and obtains the long-term tidal forecast based on the establishment of a mathematical model of the tide. It is not yet possible to analyze the impact of non-cyclical factors. At present, the commonly used neural network forecasting method is to use various elements that affect the tide, such as celestial body position parameters, wind, air pressure, precipitation, etc. Learn to determine the parame...

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Problems solved by technology

According to the established neural network model and input for tidal forecasting, although this method makes up for the shortcomings of harmonic analysis that cannot predict non-periodic factors to a certain extent, the samples for learning and training require a large amount of data, involve a wide range, and can cover various possible occurrences. situation, while the historical data of stations with non-periodic factors are generally less

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