滑坡变形组合预测方法的研究

摘要

For predicting a mine’s slope deformation so as to adopt timely safety measures,this paper proposes a new dynamic synthetical prediction method.With the help of phase-space reconstruction by substituting different lag time,a new data series from the original monitoring data was get for this method.It took the linear optimization method,which on the condition of the minimum sum of squares of prediction residue error,to calculate the weights of different methods’predictions,such as ARMA time series and GM(1,1)grey theory.The comparison of the results indicates that,the standard deviation of the synthetical prediction is reduced obviously compared with independent approaches;predictions after the Phase-space reconstruction can improve the average precision.And this provides a reliable method for the forecast and precaution for each kind of landslide.%为了预测边坡变形大小，以便及时采取防治措施，通过取不同的迟滞时间对原始监测数据进行相空间重置获得新的数据序列，再以这些新的数据序列为基础，采用预测残差平方和最小的线性优化方法，将 ARMA 时间序列法和 GM（1，1）灰色理论的预测结果进行组合，对某矿的边坡变形量进行滚动组合预测。对不同方法的预测精度和预测残差的标准差进行了对比分析，结果对比表明，组合预测较单项预测方法残差标准差明显减小，且相空间重置后的组合预测结果提高了直接预测的平均预测精度，这为边坡变形趋势的预测与防灾预警提供了可靠的方法。