Wave element prediction method and system

Abstract

The invention discloses a wave element prediction method and system. The method comprises the following steps: acquiring wind field information; performing pre-prediction according to the wind field information to perform optimization verification on the initial model to obtain a set prediction model; performing formal prediction according to the set prediction model to obtain a prediction sequence value; checking the prediction sequence value, and determining a prediction result meeting a verification requirement; performing prediction result classification processing on the prediction results through SOM machine learning to obtain prediction model sets with different precisions; selecting a target precision prediction model from the precision prediction model set, and adding a correction value to a prediction result of the target precision prediction model as a final prediction result of the output wave element; wherein the wave element prediction result comprises but is not limited to effective wave height data, wave crest period data and wave direction data. The method is small in error and good in prediction effect, and can be widely applied to the technical field of data processing.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a wave element prediction method and system. Background technique [0002] The importance of waves comes from the different studies and applications that occur at sea, such as navigational safety, ship routing, oil and gas production and transportation, arising from the use of platforms and pipelines, respectively. In addition, waves also have an important impact on near-shore engineering, such as port operations, ship stability conditions in harbors, coastal vulnerability assessments, and coastal structural design and verification. The potential increase, decrease and breakage of waves is a key factor in the variation of coastal silting processes and in the actual performance of devices that convert wave energy into electricity. [0003] The existing wave forecasting methods are relatively simple. For example, single deterministic forecasting, the forecasting effect of...

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Problems solved by technology

[0003] The existing wave forecasting methods are relatively simple

For example, single deterministic forecasting, the forecasting effect of this forecasting method within 24 hours initially meets the requirements, but the forecasting effect in the long-term period is not good, and it is difficult to meet the actual needs. It has a specific use target and working environment

The forecast of machine learning will lead to increasing error as the forecast period becomes longer

Moreover, most of the existing technologies focus on the prediction of effective wave height. In actual engineering, data such as wave wind cycle and wave direction are also important, but existing technologies cannot predict

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