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State Affine Transfer Learning Method for Hydrodynamic Pressure-Driven Landslide
Liu Yong, Li Xingrui, Zhan Weiwen, Li Bingchen, Guo Jingkai, Zhong Liang
PDF(5666 KB)
PDF(5666 KB)
State Affine Transfer Learning Method for Hydrodynamic Pressure-Driven Landslide
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The hydrodynamic pressure-driven landslides in the Three Gorges reservoir area have the characteristics of stepped deformation, and it is difficult to complete the analysis and prediction of landslides accurately and reasonably under the condition of insufficient monitoring data. In view of insufficient monitoring data, a state affine transfer learning method (SATLM) was designed in this paper to analyze the state of landslides with insufficient data by learning similar landslide knowledge. In order to verify the effectiveness of SATLM in landslide state analysis, a state similarity analysis method was designed in this paper. After learning the knowledge of multiple landslides in the reservoir area, another landslide displacement prediction with insufficient data was realized.The results show that compared with BPNN and SVM, the mean absolute error and root mean square error of landslide displacement prediction of Wanzhou Tangjiao No.1 landslide are greatly reduced after state affine migration.The successful knowledge transfer of Baijiabao landslide, Baishuihe landslide, Bazimen landslide proves that the state affine transfer learning method has a good effect on the knowledge transfer of similar hydrodynamic pressure-driven landslides.
landslide / displacement mutation point / landslide state / state affine transfer / displacement prediction / hazard geology
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