STEOF-LSTM-based marine environment element prediction method

Abstract

The invention belongs to the technical field of marine dynamic environment element prediction, and particularly relates to a STEOF-LSTM-based marine environment element prediction method. On the basis of large-range and long-time ocean reanalysis data, the law of the ocean dynamic environment elements is mined through a time domain multi-scale analysis and deep learning method, and a statistical prediction model oriented to the ocean dynamic environment elements is constructed, so that medium-and-long-term time-space statistical prediction of the ocean dynamic environment elements is realized. The method can effectively make up for the defect that a traditional numerical forecasting method is short in marine dynamic environment element forecasting timeliness due to weather-driven timeliness limitation, and occupies less computing resources. The method greatly improves the medium and long term prediction capability of the ocean dynamic environment elements, provides technical support for solving the technical problem of large-range and long-period ocean dynamic environment element prediction after the ocean numerical prediction product fails, and has relatively high scientific significance and application value.

Description

technical field [0001] The invention belongs to the technical field of prediction of marine dynamic environment elements, and in particular relates to a prediction method of marine environment elements based on STEOF-LSTM. Background technique [0002] Ocean forecasting mainly includes two modes: numerical forecasting and statistical forecasting. Although numerical forecasting is the main means of marine environment forecasting at the present stage, it has disadvantages such as large amount of computation, strong sensitivity to initial conditions, and limited by timeliness. Therefore, there is an urgent need for a forecasting method that has less computational complexity than numerical forecasting and is not limited by timeliness to achieve rapid and accurate forecasting of marine dynamic environment elements. [0003] Statistical forecasting method is one of the important means in marine forecasting. When the sample data is large enough, it can establish a data-driven fore...

AI Technical Summary

Problems solved by technology

Manual classification and recognition methods are affected by subjective factors and cannot truly describe the hidden information in the data; ocean model simulation has shortcomings such as large amount of calculation, inaccurate initial conditions, and timeliness limitations; while traditional statistical analysis is not suitable for complex ocean The process cannot obtain better results through complex formulas and tedious calculations

Moreover, most of the marine spatio-temporal data are unstructured or semi-structured data, and the relationship between the data is complex or unrelated, which poses challenges to traditional statistical analysis and ocean model simulation

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