基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究

doi:10.13722/j.cnki.jrme.2015.02.017

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摘要滑坡位移预测模型的选择及其参数的选取是滑坡位移预测中至关重要的2个问题，以往的模型在预测滑坡位移时具有诸多的限制和不足。以三峡库区白水河滑坡为研究对象，基于时间序列分析方法，分离提取出滑坡趋势性位移与周期性位移。前者主要受控于滑坡的内部因素(物质组成、地质构造、地形地貌等)，可用最小二乘法对其进行多项式拟合并预测；后者是由外部影响因素(季节性降雨、库水位变动等)导致，取当月降雨量、双月降雨量、库水位高程、月间库水位变化量、双月库水位变化量和年间累计位移增量作为周期性位移的影响因子，提出采用可优化选参的粒子群优化算法(PSO)与支持向量机回归(SVR)相结合的方法对其进行预测；将各分位移预测值叠加得到累计位移预测值。运用多种方法进行分析对比，结果表明，基于时间序列与PSO-SVR耦合模型的滑坡位移预测精度要明显高于BP神经网络模型、网格搜索法优化的支持向量机模型(GS)，其在滑坡位移预测中具有一定的理论基础和良好的应用前景。

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关键词 ：边坡工程, 滑坡, 位移预测, 时间序列, 粒子群优化算法, 支持向量机回归

Abstract：The choices of landslide displacement prediction models and relevant parameters are two of the most important issues in the landslide displacement prediction. The existing models have many limitations and shortcomings in predicting the landslide displacements. In this study，the trend displacement and the periodic displacement of Baishuihe landslide in the Three Gorges Reservoir area were separated based on the method of time series analysis. The former was considered to be mainly controlled by the internal factors of landslide(the composition，the geological structure，the topography，etc.)，and was fitted and predicted with the polynomial function. The latter was caused by the external influence factors(the seasonal rainfall，the water level changes of reservoir，etc.). Taking the rainfall of current month，the cumulative rainfall of anterior two months，the reservoir level，the fluctuation of reservoir level of current month，the fluctuation of reservoir level of anterior two months and the cumulative increment of total displacement in current year as the influencing factors of periodic displacement，a combination of the particle swarm optimization algorithm(PSO) optimizing the parameters of model and the support vector machine regression(SVR) method for the periodic displacement prediction was proposed. The predicted values of the two kinds of displacements were superimposed to obtain the cumulative displacement. The result showed that the landslide displacements based on the time series prediction and PSO-SVR model were better than those from the grid search SVM and the BP neural network models.

Key words： slope engineering landslide displacement prediction time series particle swarm optimization algorithm support vector regression

收稿日期: 2014-03-17

引用本文:

张俊1，殷坤龙1，王佳佳1，黄发明2. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报, 2015, 34(02): 382-391.
ZHANG Jun1，YIN Kunlong1，WANG Jiajia1，HUANG Faming2. DISPLACEMENT PREDICTION OF BAISHUIHE LANDSLIDE BASED ON TIME SERIES AND PSO-SVR MODEL. , 2015, 34(02): 382-391.

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https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2015.02.017 或 https://rockmech.whrsm.ac.cn/CN/Y2015/V34/I02/382

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