• Remote Sensing Technology and Application
  • Vol. 39, Issue 2, 381 (2024)
Qinghe YU*, Yulong BAI, and Manhong FAN
Author Affiliations
  • College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China
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    DOI: 10.11873/j.issn.1004-0323.2024.2.0381 Cite this Article
    Qinghe YU, Yulong BAI, Manhong FAN. Data-driven Data Assimilation Method based on Support Vector Machine Algorithm[J]. Remote Sensing Technology and Application, 2024, 39(2): 381 Copy Citation Text show less

    Abstract

    Data-driven modeling is to discover the spatio-temporal evolution of state variables from data. Data-driven data assimilation is a scientific method to optimize the fusion of observation information and model by using data-driven model instead of traditional (physics-based) model. In this work, a data-driven support vector machine regression prediction model is applied to the ensemble Kalman filtering process,and the dynamic system is reconstructed from the sample set by non-parametric sampling of the dynamic system trajectory using the simulation prediction method. A data driven data assimilation method based on support vector machine regression machine learning simulation prediction strategy is proposed and applied to classical pattern driven data assimilation system. The Lorenz-63 and Lorenz-96 model are used for numerical experiments. The data assimilation performance is compared by changing the sensitivity parameters such as sample sizes,noise variance and observation step sizes. The results show that the proposed method is superior to the general sequential data assimilation method for large sample sets,which proves the effectiveness of the new method.
    Qinghe YU, Yulong BAI, Manhong FAN. Data-driven Data Assimilation Method based on Support Vector Machine Algorithm[J]. Remote Sensing Technology and Application, 2024, 39(2): 381
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