A spatio-temporal prediction method of large-scale ground subsidence considering spatial heterogeneity

The rapid and uneven ground subsidence has threatened human production activities, and high-precision subsidence prediction results are of great significance for the precise prevention and control of geological disasters. In order to grasp the evolution law of ground subsidence, a number of prediction studies have been carried out using field observation data or InSAR data. However, due to the existence of spatial heterogeneity, accurate prediction of large-scale ground subsidence is still a challenge. In this study, a spatio-temporal prediction method considering spatial heterogeneity for large-scale ground subsidence STLSTM (Spatio-temporal Long Short-Term Memory) is proposed from a data-driven perspective. First, clustering is used to identify homogenous subregions in geographic space; then, in each subregion, a special Long Short-Term Memory (LSTM) networks are used to capture the nonlinearity features of local locations; Finally, the pre-trained network is used to quantitatively predict the ground subsidence at the future time. In the experimental part, the sentinel-1 image data was used to compare the performance of STLSTM with the other 8 benchmark methods, and the effectiveness of STLSTM was analyzed using spatial statistical indicators. The results show that STLSTM achieves the highest prediction accuracy (71.4%) within 152 secs, and can effectively weaken the effect of spatial heterogeneity on large-scale subsidence prediction tasks. In conclusion, this paper integrates the spatial heterogeneity processing strategy into the deep learning model, and large-scale subsidence prediction is realized with high precision and time efficiency. © 2022 Science Press. All rights reserved.