Zhang Junrong, Tang Huiming, Wen Tao, Ma Junwei, Tan Qinwen, Xia Ding, Liu Xiao, Zhang Yongquan
Faculty of Engineering, China University of Geosciences, Wuhan 430074, Hubei, China.
School of Geosciences, Yangtze University, Wuhan 430100, Hubei, China.
Sensors (Basel). 2020 Jul 31;20(15):4287. doi: 10.3390/s20154287.
Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and -test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.
准确预测滑坡的地表位移至关重要且十分必要。然而，现有的大多数研究都忽略了诱发因素的频率成分及其对滑坡变形的影响。因此，本文提出了一种基于时间序列理论和各种智能算法的混合位移预测模型，以研究频率成分的影响。首先，利用完备总体经验模态分解（CEEMD）将三峡库区（TGRA）滑坡的监测位移分解为趋势成分和周期成分。趋势成分可通过最小二乘法进行预测。然后，分别利用CEEMD和t检验将降雨和库水位等诱发因素的时间序列重构为高频成分和低频成分。通过动态时间规整（DTW）方法从频率成分和其他常见因素（如当月降雨量）中选取主导因素。最后，将基于蚁群优化的支持向量机回归（ACO-SVR）应用于三峡库区的预测。结果表明，考虑滑坡诱发因素的频率成分后，基于ACO-SVR的位移预测模型的精度优于基于SVR和GA-SVR的其他模型。
