Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

Abstract

In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

Advance Landslide Prediction and Warning Model Based on Stacking Fusion Algorithm

“…If the value is too large, it will lead to false associations in the case of limited samples. When B(N) = N 0.6 [50,51], the effect is best, and this value is used in this study.…”

“…Similar to real-time landslide displacement monitoring, the same warning thresholds and warning levels are adopted in the advance prediction models. In this study, landslide displacement is predicted based on an LSTM model, a BiLSTM model, an LSTM-FC model [49], a double-BiLSTM model [50] and an LMD-BiLSTM model [51]. A comparison between the results and the actual landslide displacement values is shown in Figure 9.…”

“…Therefore, an advance landslide prediction and warning model based on a stacking fusion algorithm is proposed in this paper. This model uses a deep learning stacking integration and fusion algorithm, combined with multiple landslide displacement prediction models previously studied by the author [49][50][51]. Advance prediction and warning technology is established to provide advance landslide warning.…”

“…Combined with the previous findings of the research team, five improved models [49][50][51] based on an LSTM or a BiLSTM model are selected as the basic models for the first layer of the stacking model fusion system, and the LightGBM model is used as the second-layer prediction model. The forecasting performance of the stacking model is better than that of individual models.…”

Capture and Prediction of Rainfall-Induced Landslide Warning Signals Using an Attention-Based Temporal Convolutional Neural Network and Entropy Weight Methods

“…Gong et al considered the problem of interval prediction of landslide displacements and proposed a new method of interval prediction of landslide displacements combining dual-output least squares support vector machine (DO-LSSVM) and particle swarm optimization (PSO) algorithms [11]. Time series analysis and long short-term memory neural networks are used in landslide displacement prediction [12,13]. Lin et al analyzed the internal relationship between rainfall, reservoir water level, and periodic landslide displacement and used the double-bidirectional long short-term memory (Double-BiLSTM) model to predict landslide displacement [14].…”