A Wavelet Height Prediction Method Based on Wavelet Decomposition-Neural Network

Abstract

The invention discloses a wave height prediction method based on wavelet decomposition-neural network, which belongs to the technical field of marine monitoring. The time series is generated by buoy data, and the time series is decomposed and reconstructed to solve the clutter in the sequence; The data is used for the processing of neural network training samples; training of neural network models, wavelet decomposition and reconstruction of time series, continuous optimization of training samples and wavelet decomposition layers during training, and establishment of corresponding wavelet decomposition-neural networks for training using feedback neural networks Network model; use the test samples established in the neural network to test the wavelet decomposition-neural network model with test samples. The invention can predict the extreme wave height of sea waves for a period of time in the future, so as to provide strong guarantee for sea operations and sea navigation.

Description

technical field [0001] The invention relates to the technical field of marine monitoring, in particular to a wave height prediction method based on wavelet decomposition-neural network. Background technique [0002] With the continuous development and utilization of land resources, the land resources are constantly depleted. Humans began to turn their attention to the resource-rich ocean, and the ocean has gradually become an important way for humans to obtain resources. However, the weather at sea is complex and changeable, and it is an important source of danger during marine operations. How to timely and accurately forecast extreme weather at sea, especially the extreme wave height of waves, has become the focus of research by experts and scholars today. [0003] The physical process of generating a series of waves by means of wind is very complex, unstable, non-linear, and uncertain. So far, the research on this physical process has not yet matured. The prediction of w...

AI Technical Summary

Problems solved by technology

Numerical methods are more accurate than parametric methods, can provide information at multiple locations simultaneously, and are more plausible when wind speed varies with its direction and area over a given duration, but require a large number of oceanographic and meteorological parameters , which increases the prediction cost in actual prediction and requires a large number of oceanographic and meteorological parameters, resulting in heavy data acquisition tasks and reduced operability

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