Urban surface deformation monitoring and prediction by integrating SBAS-InSAR and Elman neural network

AbstractThe existing prediction methods have complex model application, high requirements for data parameters, and are limited to the prediction of a single observation point. To address this problem, this paper proposes a deep learning-based surface subsidence prediction method. Taking Hefei City of China as the research area, the time-series surface deformation results of this area are obtained by using SBAS-InSAR, and then the SFLA intelligent algorithm and Elman neural network model are combined to predict the surface deformation of key urban areas, and the prediction results are compared and analyzed.The experimental results show that the prediction model proposed in this paper can not only accurately predict a single deformation point, but also predict regional land subsidence, and can be used for auxiliary decision-making of urban spatial planning, early warning of geological hazards and hazard mitigation.Keywords: Urban surface deformationSBAS-InSARLarge-scale deformation predictionTime-series predictionElman neural networkSFLA Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was funded by the National Natural Science Foundation of China [grant numbers 52074010, 41602357] and Natural Science Foundation for Distinguished Young Scholars of Anhui Province [grant number 2108085Y20].Notes on contributorsChaoqun TengChaoqun Teng (1997–) is currently a master candidate at Anhui University of Science and Technology, and his current research focuses on InSAR data processing and applications.Lei WangLei Wang (1984–), Professor, is now a member of International Association of Mine Surveying, graduated from China University of Mining and Technology, has published more than 30 SCI/EI papers, authorised 6 invention patents, won the first prize of surveying and mapping science and technology of China Society of Surveying and Mapping, his main research interests are focused on InSAR/TLS deformation monitoring, data processing and mine disaster monitoring, prediction and control.Chuang JiangChuang Jiang (1993–), Ph.D., graduated from Anhui University of Science and Technology. His research mainly focuses on the monitoring, prediction and control of mine disasters, identification and protection of mining damage, and new technologies of mine and underground engineering survey.