Deformation Prediction of Cihaxia Landslide Using InSAR and Deep Learning

Abstract

Slope deformation monitoring and analysis are significant in the geological survey of hydraulic engineering. However, predicting future slope deformation is a vital and challenging task for engineers. The accurate estimation of slope displacement is required for the risk assessment of slope stability. This study was conducted using slope deformation data obtained by interferometric synthetic aperture radar. Five typical points of the slope in different zones were selected to establish the prediction model. Based on the observed data, a prediction model based on long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) was proposed. Firstly, ARIMA and LSTM models were used separately to predict slope deformation. Root mean square error, mean absolute error, and R2 were used to evaluate the performance of the models, and the results showed that LSTM is more effective than ARIMA. It denotes that the LSTM model can catch the trend in the data sequence with time, and ARIMA is good at predicting the bias in the stationary data sequence. Then, the predictions of ARIMA were added to the original data while the new data were fed to the LSTM model. For most data points, our LSTM-ARIMA model achieved good performance, indicating that the model is robust in slope deformation prediction. The effectiveness of the proposed LSTM-ARIMA model will enable engineers to take corresponding measures to prevent accidents before landslides occur.