Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

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

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.

Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predictions in the slope-sliding process. In this paper, we used Python to develop the landslide displacement-prediction method based on the eXtreme Gradient Boosting (XGBoost) algorithm, and optimized the hyperparameters through a genetic algorithm to solve the problem of insufficient short-term displacement-prediction accuracy for landslides. We compared the deviation, relative error (RE) and median of RE of predicted values obtained using XGBoost, SVR and RNNs, and the actual value of landslide displacement. The results show that the accuracies of slope displacement prediction using XGBoost and SVR are very high, and that using RNNs is very low during the sliding process. For large displacement values and small numbers of samples, the displacement-prediction effect of XGBoost algorithm is better than that of SVR and RNNs in the sliding process of landslide. There are generally only fewer data samples collected during the landslide sliding process, so RNNs is not suitable for displacement prediction in this scenario. If the number of data samples is large enough, using RNNs to predict the long-term displacement of the slope may also have a much higher accuracy.

Landslide Displacement Prediction Model Using Time Series Analysis Method and Modified LSTM Model

Landslides are serious and complex geological and natural disasters that threaten the safety of people’s health and wealth worldwide. To face this challenge, a landslide displacement prediction model based on time series analysis and modified long short-term memory (LSTM) model is proposed in this paper. Considering that data from different time periods have different time values, the weighted moving average (WMA) method is adopted to decompose the cumulative landslide displacement into the displacement trend and periodic displacement. To predict the displacement trend, we combined the displacement trend of landslides in the early stage with an LSTM model. Considering the repeatability and periodicity of rainfall and reservoir water level in every cycle, a long short-term memory fully connected (LSTM-FC) model was constructed by adding a fully connected layer to the traditional LSTM model to predict periodic displacement. The two predicted displacements were added to obtain the final landslide predicted displacement. In this paper, under the same conditions, we used a polynomial function algorithm to compare and predict the displacement trend with the LSTM model and used the LSTM-FC model to compare and predict the displacement trend with eight other commonly used algorithms. Two prediction results indicate that the modified prediction model is able to effectively predict landslide displacement.