西安科技大学学报2026，Vol.46Issue(3)：482-495,14.DOI:10.13800/j.cnki.xakjdxxb.2026.0302
基于FCM-STGCN模型的露天矿边坡形变时空预测方法
Spatiotemporal prediction method of slope deformation in open-pit mine based on FCM-STGCN model
摘要
Abstract
Open-pit coal mining easily induces slope instability and deformation,to investigate an ef-fective monitoring and prediction methods,an open-pit mine in Qinghai Province was taken as the study area.Small baseline subset interferometric synthetic aperture radar(SBAS-InSAR)was used to obtain time-series slope deformation data for the study area,and monitoring points with different deformation magnitudes were clustered into subregions by fuzzy C-means(FCM).The optimal number of clusters was determined by the DB index and silhouette coefficient.Under the optimal number of clusters,de-formation points with similar deformation characteristics were divided into the same subregion by the FCM algorithm.The FCM-STGCN model was then constructed by combining the clustering algorithm with a spatiotemporal graph convolutional network(STGCN)to predict slope deformation trends.The results show that under three different training-set and prediction-set ratios,the FCM-STGCN model a-chieves the best prediction performance compared with the long short-term memory(LSTM)model and the FCM-LSTM model.Under the optimal scenario,the root mean square error,mean absolute error,weighted mean absolute percentage error,and coefficient of determination of the FCM-STGCN model are 4.2 mm,3.1 mm,6.4%,and 0.996,respectively.The minimum difference between Moran's I of the predicted values and that of the true values is as low as 0.007,indicating a high consistency in spa-tial distribution characteristics.This study achieves high-precision prediction of slope deformation and can provide a theoretical basis for slope deformation prediction and disaster prevention in open-pit coal mines.关键词
露天煤矿/边坡形变/空间相关/聚类算法/时空图卷积神经网络Key words
open-pit coal mine/slope deformation/spatial correlation/clustering algorithm/spatiotem-poral graph convolutional neural network分类
矿业与冶金引用本文复制引用
李树刚,王锴,徐培耘,葛佳琪,李文静,田雨,张晓龙..基于FCM-STGCN模型的露天矿边坡形变时空预测方法[J].西安科技大学学报,2026,46(3):482-495,14.基金项目
国家重点研发计划项目(2022YFF1302601) （2022YFF1302601）
