重庆大学学报2025，Vol.48Issue(10)：81-94,14.DOI:10.11835/j.issn.1000-582X.2025.10.008
基于北斗监测数据的高陡边坡变形Transformer-CNN预测模型
Transformer-CNN prediction model of high and steep slope deformation based on Beidou detection data
摘要
Abstract
High and steep slopes are common during the construction of large-scale projects,and their deformation often leads to geological hazards,posing significant threats to life and property.Efficiently collecting displacement data and developing an accurate predictive model are therefore essential.This study proposes a Transformer-CNN hybrid model that integrates convolutional layers and residual structures into the Transformer architecture.The optimized model is applied to displacement data obtained from the Beidou satellite system in a large water conservancy project in Chongqing.Experimental results indicate that the Transformer-CNN model achieves lower MAE,MSE,and RMSE values compared to single-model approaches,demonstrating superior prediction accuracy.These findings suggest that the proposed model offers a practical solution for predicting and analyzing slope deformation in similar engineering projects.关键词
Transformer-CNN/北斗数据集/时间序列/位移预测/高陡边坡变形Key words
Transformer-CNN/Beidou dataset/time series/displacement prediction/slope deformation分类
信息技术与安全科学引用本文复制引用
伊廷婧汶,黄才生,覃勇,宋治江,贺小含,桂镜骑,王楷..基于北斗监测数据的高陡边坡变形Transformer-CNN预测模型[J].重庆大学学报,2025,48(10):81-94,14.基金项目
重庆市水利科技项目(CQSLK-2023028) （CQSLK-2023028）
重庆市教育委员会科学技术研究计划(KJZD-K202500303).Supported by Chongqing Water Conservancy Science and Technology Project(CQSLK-2023028)and Municipal Education Commission Science and Technology Research Plan(KJZD-K202500303). （KJZD-K202500303）
