State Affine Transfer Learning Method for Hydrodynamic Pressure-Driven Landslide
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摘要: 三峡库区的动水驱动型滑坡具有阶梯式变形特征，在监测数据不足的情况下，难以准确、合理地完成滑坡分析与预测预报等相关研究.针对监测数据不足的情况，设计了一种状态仿射迁移学习方法（State affine transfer learning method，SATLM），通过学习相似滑坡的知识完成对数据量不足的滑坡状态分析.为验证SATLM对滑坡状态分析的有效性，设计了一种状态相似分析方法，完成对库区多个滑坡的知识学习后实现对另一个数据量不足的滑坡地表位移预测.结果表明，完成状态仿射迁移后，本方法与BPNN和SVM相比，万州塘角1号滑坡地表位移预测的平均绝对误差和均方根误差都实现了较大降低.白家包滑坡、白水河滑坡、八字门滑坡知识的成功迁移，证明了SATLM在相似动水驱动型滑坡的知识迁移上具有较好效果.Abstract: 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.
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图 4 白家包滑坡、白水河滑坡、八字门滑坡、万州塘角1号滑坡位置关系
Fig. 4. The locations of Baijiabao landslide, Baishuihe landslide, Bazimen landslide, Wanzhou Tangjiao No.1 landslide
图 5 白家包滑坡(a)、白水河滑坡(b)、八字门滑坡(c)、万州塘角1号滑坡(d)剖面图(Long et al., 2022)
Fig. 5. The cross-section of Baijiabao landslide (a), Baishuihe landslide (b), Bazimen landslide (c), Wanzhou Tangjiao No.1 landslide (d) (Long et al., 2022)
图 6 白家包滑坡(a)、白水河滑坡(b)、八字门滑坡(c)、万州塘角1号滑坡(d)GPS监测点分布图(Long et al., 2022)
Fig. 6. The locations of GPS monioring sites of (a) Baijiabao landslide, (b) Baishuihe landslide, (c) Bazimen landslide, (d) Wanzhou Tangjiao No.1 landslide (Long et al., 2022)
图 7 白家包滑坡(a)、白水河滑坡(b)、八字门滑坡(c)、万州塘角1号滑坡(d)累积地表位移、库水位、降雨量关系(Li et al., 2021)
Fig. 7. Relationship between the cumulative displacement, reservoir water level and precipitation of Baijiabao landslide (a), Baishuihe landslide (b), Bazimen landslide (c), Wanzhou Tangjiao No.1 landslide (d) (Li et al., 2021)
图 8 万州塘角1号滑坡地表位移预测对比
Fig. 8. The comparison of prediction results of Wanzhou Tangjiao No.1 landslide
表 1 滑坡地表位移与影响因子灰色关联度统计
Table 1. Grey correlation degree statistics of landslide displacements and influence factors
监测点
名称Rt‒2 Rt‒1 Rt Kt‒2 Kt‒1 Kt △Wt‒2 △Wt‒1 源域 ZG323 0.74 0.76 0.75 0.74 0.76 0.76 0.84 0.88 ZG324 0.73 0.76 0.74 0.75 0.76 0.75 0.85 0.88 ZG325 0.73 0.75 0.74 0.74 0.76 0.75 0.84 0.88 ZG326 0.73 0.75 0.73 0.74 0.76 0.75 0.84 0.88 ZG93 0.75 0.75 0.76 0.72 0.74 0.75 0.77 0.80 ZG110 0.73 0.72 0.71 0.75 0.71 0.69 0.70 0.67 ZG111 0.67 0.73 0.72 0.72 0.72 0.67 0.65 0.61 目标域 WZ13-09 0.67 0.66 0.73 0.81 0.81 0.81 0.75 0.75 
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表 2 滑坡状态的分类情况
Table 2. Classification of landslide state
ZG323 ZG324 ZG325 ZG326 突变状态时间段 2009.06
2010.07
2011.06
2012.06
2013.062009.06
2010.07
2011.06
2012.06
2013.062009.06
2010.07
2011.06
2012.06
2013.062009.06
2010.07
2011.06
2012.06
2013.06蠕变状态时间段 2008.03~2009.05
2009.10~2010.06
2010.10~2011.05
2011.10~2012.05
2012.10~2013.05
2013.10~2013.122008.03~2009.05
2009.10~2010.06
2010.10~2011.05
2011.10~2012.05
2012.10~2013.05
2013.10~2013.122008.03~2009.05
2009.10~2010.06
2010.10~2011.05
2011.10~2012.05
2012.10~2013.05
2013.10~2013.122008.03~2009.05
2009.11~2010.06
2010.11~2011.05
2011.10~2012.05
2012.10~2013.05
2013.10~2013.12ZG93 ZG110 ZG111 WZ13-09 突变状态时间段 2010.07
2011.07
2012.06
2013.072009.06
2010.07
2011.06
2012.07
2013.062008.09
2009.06
2010.07
2011.06
2012.07
2013.062012.04
2013.05蠕变状态时间段 2010.03~2010.06
2010.12~2011.06
2011.10~2012.05
2012.11~2013.06
2013.10~2013.122008.03~2009.05
2009.10~2010.06
2010.10~2011.05
2011.10~2012.06
2012.10~2013.05
2013.09~2013.122008.03~2008.08
2008.12~2009.05
2009.10~2010.06
2010.10~2011.05
2011.10~2012.06
2012.10~2013.05
2013.09~2013.122011.03~2012.03
2012.07~2013.04
2013.08~2014.05
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表 3 3种方法预测结果的RMSE值和MAE值
Table 3. The RMSE and MAE values of three methods
RMSE MAE SSAM 2.5 1.9 BPNN 8.3 5.4 SVM 8.5 5.1
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