Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

Table 1

The characteristics of some models of predicting slope deformation.

Category	Commonly used model	Input data or characteristics	Limitations
Statistics-based model	Auto-regressive moving average model (ARMA)	The correlation analysis of the input series is required, and the order and parameters of the model should be determined in advance.	Since a variety of factors affect landslide deformation, the statistical model cannot reflect the fluctuation in the landslide displacement with the influencing factors.
	Gray model and related improved models	The original time series should be accumulated to yield the once accumulating generation operator (1-AGO) data sequence.
Machine-learning-based model	Extreme learning machine model	The inputs are usually the selected inducing factors or features.	Static fitting is performed on historical data, which cannot reflect the nature of the dynamic evolution of landslides
	Support-vector machine (SVM) model
	Neural network model
	Deep-learning-based models, such as long short-term memory (LSTM) and gated recurrent unit (GRU) models	The input can be original displacement series and various influencing factors.	When the sample data are limited, overfitting can easily occur, resulting in poor prediction accuracy.