Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China
Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China
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DOI：
10.1016/j.enggeo.2016.02.009
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发表时间：
2016-04
影响因子：
7.4
通讯作者：
Chao Zhou;K. Yin;Ying Cao;B. Ahmed
中科院分区：
文献类型：
--
作者：
Chao Zhou;K. Yin;Ying Cao;B. Ahmed
三峡库区滑坡位移受降雨和水库周期性调度的影响，呈台阶状变形。针对滑坡位移的阶跃特性，提出了基于诱发因素响应的粒子群优化与支持向量机（PSO-SVM）耦合模型，并对滑坡位移进行预测。采用滑动平均法将总位移分为趋势项和周期项。趋势位移受地质条件控制并由多项式函数预测，而周期位移受触发因素和滑坡演化状态的共同控制。为此，提出了基于降水量、水库变化幅度和前期位移量等因素的PSO-SVM预测模型。以三峡库区典型的台阶式滑坡--八字门滑坡为例，对预测结果进行了验证。均方根误差和平均绝对百分比误差的值分别为13.28和25.95。结果表明，降雨和库水位是滑坡变形的主导因素。滑坡的演化状态对反映滑坡位移与诱发因素之间的响应关系也具有重要意义。结果表明，PSO-SVM模型能较好地反映各因素与周期位移之间的响应关系，使总位移预测值与实测值吻合较好。
The landslide displacement in the Three Gorges Reservoir, China, experiences step-like deformation that is influenced by rainfall and the periodic scheduling of the reservoir. In view of the step-like characteristic, the Particle Swarm Optimization and Support Vector Machine (PSO–SVM) coupling model based on the response of the induced factors was proposed to predict the landslide displacement. The moving average method was adopted to divide the total displacement into trend term and periodic term. The trend displacement was controlled by the geological conditions and predicted by polynomial function, while the periodic displacement was under the combined control of the triggers and the evolution state of the landslide. Therefore, the PSO–SVM model, based on the factors of the precipitation, the variation range of the reservoir and the displacements of the prior-periods, was proposed to predict the periodic displacement. The typical step-like landslide in the Three Gorges Reservoir, which is known as the Bazimen landslide, was taken as a case study to verify the prediction results. The values of the root mean square error and the mean absolute percentage error were 13.28 and 25.95, respectively. The results showed that rainfall and reservoir water level were the dominant factors for the step-like landslide deformation. The evolution state of the landslide was also significant in reflecting the response relationship between the displacement and inducing factors. In conclusion, the proposed PSO–SVM model can better represent the response relationship between the factors and the periodic displacement, which made the predicted values of the total displacement fit with the measured values greatly.