Prediction of InSAR deformation time-series using improved LSTM deep learning model.

ABSTRACT: Mining-induced subsidence is one of the major concerns of mining industry/mine owners, statutory bodies, and environmental organisations. Therefore, mine subsidence monitoring and prediction is of utmost importance for its effective management. In the present study, a modified LSTM model is developed to predict the InSAR deformation time series. The modified LSTM model may also be extended for prediction based on time-series data in general. Further, to check the developed model's performance, InSAR deformation time-series results obtained from 26 TSX/TDX datasets of Mine-A in Khetri Copper Belt, India, are used as an input. Further obtained results from mLSTM have been compared with the other two models, namely RNN and LSTM. Efficiency comparison results reveal that RNN, LSTM, and modifie

SUBMITTER: Soni R 

PROVIDER: S-EPMC11825842 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature