GRU-based harmonic residual segmented tide level prediction method

Abstract

The invention discloses a GRU-based harmonic residual segmented tide level prediction method. According to the method, firstly, a site hourly astronomical tide sequence is calculated by using a tide harmonic analysis method according to long-term actually-measured tide level data of a site to be measured, the actually-measured hourly tide level sequence of the site is aligned according to time, the astronomical tide sequence is subtracted, and the hourly tide level reconciliation residual sequence is obtained; the harmonic residual error sequence is divided into two sections of samples as input variables by comprehensively considering the influence action time periods of characteristic factors such as monsoon and typhoon, two tide level residual error GRU prediction models are formed through training respectively, and a residual error prediction result sequence is obtained through calculation; and finally, the residual prediction result sequence is added to the astronomical tide sequence of the corresponding time sequence, and the tide level prediction result is obtained. According to the method, tide level prediction can be achieved only by using single-station tide long-time sequence data, participation of other factors is not needed, high-precision prediction of station tide level data is achieved, and the efficiency of the tide level prediction process is improved.

Description

technical field [0001] The present invention relates to the technical field of ocean tide level forecasting, more specifically relate to a kind of harmonic residual segmental tide level forecasting method based on GRU. Background technique [0002] At present, ocean tide level changes mainly include astronomical tides affected by celestial tidal forces and nonlinear tide level changes affected by marine environmental factors. The research on tidal harmony analysis methods has been very in-depth. However, due to insufficient consideration of the nonlinear tide level changes influenced by marine environmental factors, and limited by the cognition of the physical relationship, there are still large uncertainties in the analysis results obtained by the existing harmonic analysis method. [0003] At the same time, with the rapid development of artificial intelligence technology, some scholars have used neural network algorithms to carry out tide level prediction research. But in...

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Problems solved by technology

[0005] In view of this, the present invention provides a GRU-based harmonic residual segmentation tide level prediction method, which effectively solves the limitations of the existing nonlinear tide level change prediction methods in terms of analysis objects and correlation factor considerations to a certain extent and the lack of predictive accuracy

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