基于Bootstrap和GA-Elman的土质滑坡坡表变形区间预测

Prediction of soil landslide surface deformation interval based on Bootstrap and GA-Elman

坡体变形是表征边坡稳定性最直观的指标。如何科学合理地解译其演化特征,对滑坡灾害预警防范具有重大的工程意义及科学价值。由于滑坡灾害的影响因素中,很多不确定因素都不可能完全准确地定量分析,只能从定性层面建立影响因素与坡体变形的关系,为了解决滑坡确定性预测方法中未考虑预测与数据误差的问题,量化滑坡点预测结果中的不确定因素的影响,依托滑坡坡表变形点预测方法,提出基于残差Bootstrap与GA-Elman神经网络的区间预测方法。相比于传统方法,通过变形伪数据集的建立、GA-Elman模型的迭代训练与总方差估计、ELM网络残差训练与随机误差方差估计等步骤创建的区间预测方法在预测可靠性、区间宽度、针对特殊变化坡体的预测灵活度等方面都有显著提升。利用Bootstrap重抽样模型、GA-Elman神经网络预测算法以及区间预测理论方法,建立基于Bootstrap和GA-Elman的滑坡变形区间预测模型。研究导致滑坡变形的不同影响因素、预测模型参数及置信区间等对于区间预测模型效果的影响,并运用到现场滑坡中。分析结果表明,所提出的方法可适用于现场滑坡,更好地将滑坡降雨等影响因素与坡体表观变形建立联系,为滑坡灾害的提前预警与防治提供可靠准确的信息支撑。

Slope deformation has been regarded as the key factor to reveal the state of slope stability,and understanding the mechanism of slope deformation is essential for landslide prediction and prevention.However,the impact of uncertain factors on the failure behavior of slope make it difficult to verify the relation between the factors and slope deformation.To solve this problem,this paper proposed a new method based on residual Bootstrap and GA-Elman neural network to quantify the influence of uncertain factors on the slope deformation and estimate the interval for slope deformation.The new method,which was deduced from the wavelet analysis,genetic algorithm(GA),and Elman algorithm,was then applied to site in which landslide monitoring systems were installed.The predicted slope deformation based on the monitoring data based on the new proposed method is more reliable than that estimated by the traditional models.The results show that the proposed method highlighted reliability and could be used with higher accuracy to forecast the slope surface deformation in the process of landslides.