• 学报（中文） •

### 基于变分模态分解和AMPSO-SVM耦合模型的滑坡位移预测

1. 1. 上海交通大学 电子信息与电气工程学院， 上海 200240； 2. 国网湖南省电力公司 防灾减灾中心， 长沙 410007； 3. 华东师范大学 统计学院， 上海 200241
• 通讯作者: 范春菊，女，副教授，电话(Tel.): 021-34204290；E-mail：fanchunju@sjtu.edu.cn.
• 作者简介:徐峰(1992-)，男，安徽省六安市人，硕士生，主要从事电力系统保护与控制研究. E-MAIL: fengxu0520@163.com
• 基金资助:
国家电网公司科技项目(5216A01600VX)

### Displacement Prediction of Landslide Based on Variational Mode Decomposition and AMPSO-SVM Coupling Model

U Feng,FAN Chunju,XU Xunjian,LI Li,NI Jiayun

1. 1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Disaster Prevention and Reduction Center, State Grid Hunan Electric Company, Changsha 410007, China; 3. School of Statistics, East China Normal University, Shanghai 200241, China

Abstract: Landslide is a natural disaster that seriously threatens and endangers the safety of life and property of residents. Landslide displacement prediction is helpful to predict natural disasters such as landslide. Monitoring data processing and the establishment of prediction model are the basis of landslide displacement prediction. According to the shortcomings of the EMD and EEMD decomposition algorithms which are applied widely in present analysis of time series，the variational mode decomposition algorithm is applied in signal processing to the landslide displacement sequences decomposition to obtain the subsequences. The AMPSO-SVM displacement prediction coupling model is constructed by combining adaptive mutation particle swarm optimization (AMPSO) and support vector machine (SVM). Apply the coupling model to predict displacement subsequences separately， then reconstruct all the sub-sequences prediction results and the total displacement prediction value is acquired. Taking the monitoring point XD1 in the Baishuihe landslide in the Three Gorges Reservoir area as an example, different scenarios are set to clarify the validity and stability of the proposed model. The case studies show that the prediction performance of landslide displacement based on variational mode decomposition and AMPSO-SVM coupling model is superior to BP neural network prediction model and SVM model optimized by the grid search algorithm, and it has a good theoretical basis and engineering application value.