【摘要】 滑坡位移预测效果一方面取决于预测模型的优劣,另一方面取决于野外监测数据的质量。针对目前滑坡常规监测技术与评价方法的不足,本文采用光纤监测技术、监测数据与PSO-SVM预测模型相结合的评价方法,对三峡马家沟Ⅰ号滑坡的深部位移进行了预测;通过对320个滑坡深部位移光纤监测数据分析,基于时间序列法,将滑坡位移分为趋势性位移和波动性位移;趋势性位移采用拟合法进行预测,波动性位移采用PSO-SVM模型进行预测;最后将趋势项和波动项位移预测值叠加得到累积位移的预测值。研究结果表明,PSO-SVM模型对波动性位移预测的均方根误差0. 51 mm,平均绝对百分误差0. 37 mm,能准确预测滑坡波动项位移;累积位移预测值与实测值的相关系数为0. 98,均方根误差为0. 54 mm,预测效果较好,可以用来对滑坡深部位移进行短期预测。更多还原

【Abstract】 There are two main factors that influence the accuracy of landslide displacement prediction. One is the reliability of the prediction model and the other is the quality of field monitoring data. Currently,conventional landslide monitoring technology and evaluation methods have many limitations and shortcomings. In this paper,we propose a new evaluation methodology of the combination of fiber-optic monitoring technology,monitoring data and PSO-SVM prediction model. We use it to predict the deep displacement of Majiagou No. 1 landslide in the Three Gorges Reservoir. Firstly,by analyzing 320 fiber-optics monitoring data of landslide deep displacement,we decompose accumulative displacement into trend and fluctuant components based on the time series method. Then,the trend displacement is predicted with the fitting method. The fluctuant displacement is predicted with the PSOSVM model. Lastly,the prediction of cumulative displacement is computed with the predicted periodic and fluctuant displacement values. Research results show that the root mean square error is 0. 51 mm and an average absolute percentage error is 0. 37 mm,which demonstrate this model has a preferable prediction effect. The predicted total displacement show great consistency with the measured total displacement,with the RSME of 0. 54 mm and the correlation coefficient of 0. 98,respectively. This method can be used to make short-term predictions of landslide deep displacement.更多还原