Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

Urban land subsidence is a geological disaster formed by natural and human factors. Cumulative land subsidence easily causes damage to buildings, infrastructure, underground engineering, and other hazards, which seriously threaten the safety of people’s lives and property and cause national economic losses. In the face of urban land subsidence, monitoring, analyzing, and predicting spatiotemporal changes in land subsidence are necessary. The prediction of land subsidence is a crucial step for the early warning of urban infrastructure damage and establishment of a timely remedy. In this study, the Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) technique was utilized to monitor the time series land subsidence at Hong Kong International Airport from 2015 to 2020 by using 152 Sentinel-1A images with an ascending orbit. The local weighted scatter smoothing (Loess) method was used to reduce and smooth the noise in the original data of surface deformation points. Given that the advantages of LSTM correspond to the results of TS-InSAR, on the basis of TS-InSAR data, a stacked LSTM neural network was used to construct a surface deformation prediction model with two LSTM layers, two dense layers, and three dropout layers. The stacked LSTM model was employed to predict the surface deformation of the airport, and its results were compared the predicted results obtained with the true InSAR findings. The average vertical deformation rate of Hong Kong International Airport’s surface for 2015—2020 was -19—5 mm/year. The surface subsidence of the airport gradually increased, and the cumulative subsidence in the vertical direction reached 116 mm in December 2020. The cross-validation of the two time-series analysis methods and the comparison of the monitoring results with the level data showed that the InSAR monitoring results in this study had high accuracy and reliability. A stacked LSTM prediction model was established based on the time-series InSAR monitoring results, and the InSAR observation results were compared with the stacked LSTM prediction results. The root-mean-square error and mean absolute error of the predicted and true values were low, namely, 0.75 and 0.61 mm, respectively, and the correlation coefficient was 0.99. The LSTM prediction model demonstrated good performance at the point level scale and could predict ground subsidence accurately on the basis of TS-InSAR data. The stacked LSTM model was employed to predict the time-series subsidence of Hong Kong International Airport in 2021 by using TS-InSAR deformation data from 2015—2020. The analysis revealed that the long-term prediction process of the stacked LSTM prediction model became invalid after six months. Therefore, the stacked LSTM prediction model is suitable for short-term predictions with a short-term prediction scale of about six months, and the maximum cumulative vertical subsidence of the airport will reach 114 mm in June 2021. In summary, the stacked LSTM prediction model proposed in this study can be used as an effective method to predict surface deformation, and although the LSTM model is only suitable for short-term predictions, its prediction results can be used to assist in decision-making, early warning, and hazard mitigation. In the future, additional data can be incorporated into the LSTM model to accurately determine if the model is suitable for long-term predictions and improve the robustness of the prediction model. © 2022 Science Press. All rights reserved.