摘要
基于小基线集干涉测量技术(small baseline subsets interferometric synthetic aperture radar,SBAS-InSAR)和机器学习知识对高填方区域进行地表沉降监测及预测,对工程项目的施工、检修、运营等工作都具有重要的指导意义。文章以重庆东港集装箱码头为研究对象,选取2018—2019年覆盖研究区的31景Sentinel-1A数据,利用SBAS-InSAR技术获取该区域的地表沉降数据,并进行内外精度评定;通过信息量模型分析地表沉降易发地地势特点,选择预测点位;通过灰色关联分析计算动态影响因素与沉降量之间的灰色关联度,使用主成分分析法从影响因素中提取出主成分,构建训练集和测试集,通过粒子群算法-支持向量机法(particle swarm optimization-support vector regression,PSO-SVR)预测模型对测试集数据进行预测。为验证该模型在高填方区域沉降预测的可靠性和优异性,将自回归差分整合移动平均模型(autoregressive integrated moving average model,ARIMA)作为对比模型,分别将PSO-SVR模型的预测结果和ARIMA模型的预测结果与测试集进行对比。结果表明:PSO-SVR模型的预测精度优于ARIMA模型,在高填方区域地表沉降预测中具有较好的实用性。
Based on SBAS-InSAR technology and machine learning knowledge,the monitoring and prediction of surface settlement in high-fill areas have important guiding significance for construction,maintenance,and operation of engineering projects.This study takes the Chongqing Donggang Container Terminal as the research object,and utilizes 31 scenes of Sentinel-1A data from 2018 to 2019.The surface subsidence data of the area is obtained by SBAS-InSAR technology,and the internal and external accuracy is evaluated.The topography characteristics of the prone areas of surface subsidence were analyzed through an information quantity model to select prediction points.Grey Relational Analysis(GRA)was used to calculate the grey correlation degree between dynamic influencing factors and subsidence.Principal component analysis was used to extract principal components from influencing factors,and training and testing sets were constructed.PSO-SVR prediction model was used to predict the testing set data.To verify the reliability and superiority of the model in subsidence prediction in high-fill areas,the ARIMA model was used as a comparative model,and the prediction results of the PSO-SVR model and the ARIMA model were compared with the testing set.The results show that the prediction accuracy of the PSO-SVR model is better than that of the ARIMA model,and it has better practicality in predicting surface subsidence in high-fill areas.
作者
李华蓉
戴双璘
郑嘉欣
LI Huarong;DAI Shuanglin;ZHENG Jiaxin(Chongqing Smart City Institute,Chongqing Jiaotong University,Chongqing 400074,China;Changjiang Chongqing Waterway Bureau,Chongqing 401147,China;Chongqing Vocational Institute of Engineering,Chongqing 402260,China)
出处
《中国地质灾害与防治学报》
CSCD
2024年第2期127-136,共10页
The Chinese Journal of Geological Hazard and Control
基金
重庆市研究生联合培养基地项目(JDLHPYJD2020005)
重庆市自然科学基金面上项目(CSTB2023NSCQ-MSX0880)。
关键词
高填方区域
粒子群算法
支持向量机回归
形变预测
high fill area
particle swarm optimization algorithm
support vector Machine regression
deformation prediction
