基于外因响应的滑坡位移预测模型研究

根据滑坡位移序列的单调性和非线性,将滑坡位移分为趋势项和偏离量,建立曲线回归-BP神经网络模型对滑坡位移进行动态预测。以三峡库区树坪滑坡为例,首先通过曲线回归提取滑坡位移趋势项;然后在选取滑坡位移动态变化影响因子的基础上,建立BP神经网络模型逼近位移偏离量;最后将趋势项和偏离量预测值叠加,得到滑坡位移预测值。结果表明,该模型可更好地反映影响因子动态变化对滑坡位移发展的关键作用,预测的平均相对误差为3.3%,有效地提高了预测结果的精度。

Displacement Prediction Model of Landslide Based on Trigger Factors Analysis

In view of the monotony and nonlinearity of landslide displacement time series, the landslide displacement is decomposed into trend item and deviation item which are dynamically predicted by curvilinear regression-BP neural network model. The Shuping landslide in the Three Gorges reservoir area is taken as a case study. In this method, the trend item of displacement time series is extracted by curvilinear regression model and the deviation of curvilinear regression model is approximated by BP neural network model based on factors which influence displacement fluctuations. Then the prediction values of trend displacement and deviation displacement are superposed to obtain the total displacement prediction value. The results indicate that the prediction model can reflect the key role of dynamic change of impact factors in the displacement development. The average relative error of the prediction is 3.3%, indicating that the model can effectively improve the precision of prediction results.