CEEMDAN-CNN-BiLSTM混合模型矿区地表沉降预测  

Hybrid prediction model of surface subsidence deformation using CEEMDAN-CNN-BiLSTM

作　　者：王凯 肖星星 余永明[1] 贾庆磊 赵思仲 WANG Kai;XIAO Xingxing;YU Yongming;JIA Qinglei;ZHAO Sizhong(Beijing Urban Construction Survey,Design and Research Institute Co.,Ltd./Beijing Key Laboratory of Geotechnical Engineering of Deep Foundation Pit of Urban Rail Transit,Beijing 100101,China;School of Surveying,Mapping and Urban Spatial Information,Beijing University of Civil Engineering and Architecture,Beijing 102616,China)

机构地区：[1]北京城建勘测设计研究院有限责任公司/城市轨道交通深基坑岩土工程北京市重点实验室,北京100101 [2]北京建筑大学测绘与城市空间信息学院,北京102616

出　　处：《导航定位学报》2024年第5期156-163,共8页Journal of Navigation and Positioning

摘　　要：为了进一步发挥全球卫星导航系统(GNSS)实时监测优势,对时序数据中的潜藏特征与隐藏信息进行深度挖掘,提高地表沉降预测精度,提出基于自适应噪声完备集合经验模态分解(CEEMDAN)、卷积神经网络(CNN)和双向长短期记忆网络(BiLSTM)的CEEMDAN-CNN-BiLSTM混合地表沉降预测方法:以皖北某大型煤矿开采工作面与工业广场区域为验证对象,对比分析稳定区域和重点监测区域数据形态;然后基于CEEMDAN重构监测站高程数据分量,输入CNN模型提取分量隐含信息;最后构建BiLSTM模型,实现对沉降监测点位数据的短期预测。实验结果表明,相较于传统的CNN和长短期记忆模型,CEEMDAN-CNN-BiLSTM混合模型可有效降低预测误差,其中平均绝对百分比误差(MAPE)的降低范围为40%~90%,而均方根(RMS)误差的降低范围为52%~87%;该模型在时空特征捕捉和泛化能力方面表现性能较好,可为GNSS时间序列短期预测提供更为精准和可靠的解决方案。In order to further leverage the advantages of real-time monitoring with global navigation satellite system(GNSS)and explore the latent features and hidden information in time-series data,enhancing the accuracy of ground subsidence prediction,the paper proposed a hybrid prediction method of surface subsidence deformation named complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)-convolutional neural networks(CNN)-bi-directional long short-term memory(BiLSTM):focusing on a large coal mine working face and an industrial square area in northern Anhui as the validation sites,a comparative analysis of stable and critical monitoring area data morphologies was carried out;then CEEMDAN was utilized to reconstruct elevation data components at monitoring stations,and the CNN model was applied to extract implicit information from these components;finally,a BiLSTM model was constructed to achieve the short-term predictions for subsidence monitoring points.Experimental results showed that,com pared to traditional CNN and long short-term memory models,the proposed hybrid model could efficiently reduce the prediction errors;specifically,the reduction of mean absolute percentage error(MAPE)would range from 40%to 90%,and root mean square(RMS)error from 52%to 87%;in general,the performance of the proposed model could exhibit superior capabilities in capturing spatiotemporal features and generalization,providing a more accurate and reliable solution for short-term prediction of GNSS time-series data.

关 键 词：沉降预测 自动化监测 时序数据 混合模型 自适应噪声完备集合经验模态分解(CEEMDAN)-卷积神经网络(CNN)-双向长短期记忆网络(BiLSTM) 

分 类 号：P228[天文地球—大地测量学与测量工程]