Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Abstract

Accurately predicting the structural deformation trend of tunnels during operation is significant to improve the scientificity of tunnel safety maintenance. With the development of data science, structural deformation prediction methods based on time-series data have attracted attention. Auto Regressive Integrated Moving Average model (ARIMA) is a classical statistical analysis model, which is suitable for processing non-stationary time-series data. Long- and Short-Term Memory (LSTM) is a special cyclic neural network that can learn long-term dependent information in time series. Both are widely used in the field of temporal prediction. In view of the lack of time-series prediction in the tunnel deformation field, the body of this paper uses historical data of the Xinjian Road and the Dalian Road tunnel in Shanghai to propose a new way of modeling based on single points and road sections. ARIMA and LSTM models are applied in comprehensive experiments, and the results show that: (1) Both LSTM and ARIMA models have great performance for settlement and convergence deformation. (2) The overall robustness of ARIMA is better than that of LSTM, and it is more adaptable to the datasets. (3) The model prediction performance is closely related to the data quality. ARIMA has more stable performance under the lack of data volume, while LSTM has better performance with high-quality data and higher upper limit.

Analysis and Warning Prediction of Tunnel Deformation Based on Multifractal Theory

“…Cao et al proposed the complete ensemble empirical mode decomposition with adaptive noise long short-term memory (CEEMDAN-LSTM) model, combining high prediction accuracy with acceptable computational efficiency [51]. Duan et al utilized the auto regressive integrated moving average (ARIMA) and LSTM models for predicting structural deformation trends during tunnel operation, determining that LSTM performs better with high data quality and sufficient samples [52].…”

Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model

“…The gray model is suitable for analyzing and modeling incomplete systems [14][15][16][17][18], but it is mainly used for short-term and exponential growth predictions. The Autoregressive Integrated Moving Average (ARIMA) method can extract the autocorrelation of time series [19][20][21][22][23], but it requires the time series to be stable and can only capture linear relationships. Multiple regression analysis is simple and easy to use with high accuracy [24][25][26][27][28], but it has issues with multicollinearity and lacks causal inference capability.…”