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Unstructured grid significant wave height prediction method and system based on deep learning

A technology of unstructured grid and effective wave height, which is applied in neural learning methods, neural architectures, biological neural network models, etc., can solve the problems of large computing resources, consumption, and long computing time, and achieve low computing resource consumption and high processing speed Fast, avoiding effects that cannot be predicted in real time

Pending Publication Date: 2022-04-26
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

The development of deep learning has solved the shortcomings of numerical model methods that consume a large amount of computing resources and take a long time to calculate.

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  • Unstructured grid significant wave height prediction method and system based on deep learning

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Embodiment Construction

[0017] The following description and drawings illustrate specific embodiments of the invention sufficiently to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely represent possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Portions and features of some embodiments may be included in or substituted for those of other embodiments. The scope of embodiments of the present invention includes the full scope of the claims, and all available equivalents of the claims. Herein, various embodiments may be referred to individually or collectively by the term "invention", which is for convenience only and is not intended to automatically limit the scope of this application if in fact more than one invention is disclosed. A single invention or inventive concept. Herein, relational terms such...

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Abstract

The invention discloses an unstructured grid significant wave height prediction system based on deep learning, which comprises a long short-term memory (LSTM) model, and the unstructured grid significant wave height prediction system comprises a time sequence processing model composed of two LSTMs and is a recurrent neural network with an input gate, a forget gate and an output gate. The input of the model is the effective wave height data of the unstructured grid in 10000 hours provided by the FVCOM mode, and the final output is the predicted wave height in the future 1 hour, 6 hours, 12 hours and 24 hours.

Description

technical field [0001] The present invention relates to the technical field of time-space sequence prediction, in particular to a deep learning-based unstructured grid significant wave height prediction method and system. Background technique [0002] Ocean waves are an important research field in physical oceanography, and wave heights are affected by environmental changes and Earth systems, especially waves due to climate change. Sea state characteristics such as wave period, wave direction, and significant wave height are important safety factors that must be considered in activities such as ocean engineering construction, marine transportation, environmental protection, and military operations. Among them, the significant wave height of ocean waves is the most important, and accurate and reliable prediction of significant wave height is an important task in marine and engineering applications. Therefore, it is of great significance to carry out accurate operational fore...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/08G06N3/048G06N3/044
Inventor 宋弢王家荣徐丹亚魏伟韩润生孟凡
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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