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

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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.

作者王凯肖星星余永明贾庆磊赵思仲 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建筑大学测绘与城市空间信息学院

出处《导航定位学报》 CSCD 北大核心 2024年第5期156-163,共8页 Journal of Navigation and Positioning

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) ground subsidence prediction automated monitoring time series data mixed model complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)-convolutional neural networks(CNN)-bi-directional long shortterm memory(BiLSTM)

分类号 P228 [天文地球—大地测量学与测量工程]