The Prediction Method of Large-Scale Land Subsidence Based on Multi-Temporal InSAR and Machine Learning
Luo Huiyuan
Xu Qiang
Jiang Yanan
Meng Ran
Pu Chuanhao
Abstract：Land subsidence is the loss of land elevation formed by a combination of natural and human factors.To prevent the delayed progressive geohazards,it is essential to predict large-scale land subsidence with high efficiency.However,the current prediction methods usually neglect spatial characteristics of land subsidence,which are time-consuming due to the issue of single-point cycle.To address the problem,a new prediction method of large-scale land subsidence based on multi-temporal InSAR and machine learning is proposed.Firstly,the large-scale land subsidence time series information is obtained by the SBAS-InSAR technique.Secondly,the spatial modes and the consistent principal components(PCs)are extracted from the time series information with the empirical orthogonal function(EOF).Finally,the PCs are trained and predicted by predictive model based on the ridge polynomial neural network with error-output feedbacks(RPNN-EOF),and the outcomes are reconstructed back to the land subsidence time series.The 84-view Sentinel-1A data from August 2018 to May 2021 of Yan'an New District were adopted in the land subsidence time seriesacquisition.Simultaneously,the spatial modes extracted by EOF can clearly reveal the spatial variation characteristics of the whole new district.The prediction results show that the root mean square error and modeling time of the proposed method is reduced by at least 22.7%and 27.5%respectively,in comparison with that by the single-point cycle pattern and the prevailing time series methods.Thus it has good practicality and applicability.
Keywords：machine learningtime series predictionempirical orthogonal functionsspatial modalitiesneural networksmulti-temporal InSARland subsidencehazards
Publication Date：2024-05-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 1736-1745 )