【摘要】 阶跃型滑坡位移预测受多种特征因素影响，不同抽样方式的特征选择模型对预测结果影响较大。以大渡河猴子岩库区的林邦堆积体滑坡为例，旨在准确预测阶跃型滑坡位移，建立分层抽样的顺序前、后向特征选择模型、长短期记忆网络(long short-term memory network, LSTM)和注意力机制的新型综合预测模型。摒弃了传统特征选择中的随机抽样方式，采用分层抽样方法。该方法特别针对周期项位移的规律性，尤其是阶跃拐点附近的数据分布特点，显著增强了模型对阶跃段特征的表征能力。同时，引入注意力机制，赋予模型动态调整对历史时序信息关注度的能力，从而更精准地预测位移突变。结果表明：1-5监测点和2-5监测点评价指标均方根误差(root mean square error, RMSE)分别为3.16、1.56,平均绝对百分比误差(mean absolute percentage error, MAPE)分别为0.27、0.83,R~2分别为0.978、0.984。通过三峡库区白水河和八字门两处典型阶跃型滑坡进一步验证模型的鲁棒性。可见分层抽样方式结合注意力机制能有效提升阶跃型滑坡的预测精度。更多还原

【Abstract】 Step-like landslide displacement prediction is influenced by multiple characteristic factors, and the feature selection models using different sampling methods significantly affect the prediction results. Taking the Linbang accumulation body landslide in the Houziyan reservoir area of the Dadu River as an example. Step-like landslide displacement was accurately predicted through the establishment of a novel integrated prediction model that combined stratified sampling-based sequential forward and backward selection(SFS, SBS), LSTM(long short-term memory network), and an attention mechanism. Traditionally, the random sampling method in feature selection was abandoned, and a stratified sampling approach was innovatively adopted for the first time. The stratified sampling method specifically addressed the regularity of periodic displacement, particularly the data distribution characteristics near step inflection points, which significantly enhanced the model’s ability to characterize features during critical deformation stages. Additionally, an attention mechanism was introduced, endowing the model with the capability to dynamically adjust its focus on historical time-series information, thereby enabling more precise prediction of displacement mutations. The results show evaluation metrics for monitoring points 1-5 and 2-5 as RMSE(root mean square error) values of 3.16 and 1.56, MAPE(mean absolute percentage error) values of 0.27 and 0.83, and R~2 values of 0.978 and 0.984.The model’s robustness was further validated using two typical step-like landslides in the Three Gorges Reservoir area: Baishuihe and Bazimen. It is evident that the stratified sampling approach combined with the attention mechanism can effectively enhance the prediction accuracy of step-like landslides.更多还原