Ocean wave height prediction method and system, computer equipment, storage medium, and terminal

Abstract

The invention belongs to the technical field of ocean wave height prediction, and discloses an ocean wave height prediction method and system, computer equipment, a storage medium, and a terminal. The method comprises the following steps: selecting a feature with a high correlation coefficient as an input feature for predicting an ocean wave height; establishing a chaos model according to particle swarm behavior characteristics to solve the problem of premature convergence of particle swarms, wherein the CDW performs optimization in an initialization stage for a BP model, and in a CPSO-BP model, a chaotic particle swarm optimization (CPSO) algorithm searches optimal particles in a solution space to optimize initial weights and threshold values of a BP network; using parameters optimized by the CPSO algorithm as initial weights and threshold values of an ELM network to be transmitted to an ELM network; carrying out wave height prediction in the ocean early warning field by using a CPSO-BP model or a CPSO-ELM model. The deep learning method is used for wave height prediction, and the method has the advantages of high accuracy, low cost and high operation speed.

Description

technical field [0001] The invention belongs to the technical field of ocean wave height prediction, and in particular relates to an ocean wave height prediction method, system, computer equipment, storage medium, and terminal. Background technique [0002] Wave prediction is an important guarantee for marine engineering construction, marine transportation, environmental protection and military operations. In terms of port construction, waves affect the shape of the coast, which is related to the design and construction of ports, waterways and various marine buildings. Abnormal ocean fluctuations will cause incalculable damage to coasts, ports and offshore buildings, but the causes of waves are complex, and it is very difficult to predict the height of waves. The causes of ocean waves are complex, and the appearance and generation of waves are highly random. In addition to wind and waves at sea, waves may also come into contact with sea surface objects to generate new fluc...

AI Technical Summary

Problems solved by technology

[0006] (1) The traditional wave height prediction method uses a physical model to model the ocean wave, and simulates the real environment generated by the ocean wave through enough environmental data. It requires a lot of computing resources to model the ocean environment, and the resource consumption is huge.

[0007] (2) The method of using statistical methods such as the mean square height field to complete wave modeling, due to the complexity of the causes of waves, and for the diverse physical characteristics of the sea, it cannot be collected comprehensively and accurately, which makes these classic statistical methods The method cannot accurately simulate the wave motion at sea

[0008] (3) In the method of using remote sensing observation to monitor sea wave height, remote sensing satellites are expensive, lack high-performance prediction algorithms, and achieve higher precision in data processing to improve the efficiency of sea wave height prediction

[0009] Traditional methods use a lot of computing resources when solving real problems with complex environments, and the modeling algorithm takes a long time to run, making it very difficult to quickly find the optimal solution

Due to the large amount of calculation of such models, it is difficult to obtain timely and accurate prediction results from empirical models, geometric models, and physical models, and their prediction capabilities for offshore areas with complex sea conditions are limited.

In a large area of ​​sea area with complex sea conditions, it is necessary to simulate every particle in the sea area, and the numerical model will consume huge computing resources and take a lot of time for modeling and calculation

Images