Data Mining and Deep Learning for Predicting the Displacement of “Step-Like” Landslides

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R2 values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

Landslide Displacement Prediction Method Based on GA-Elman Model

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.

A New Hybrid Method for Establishing Landslide Displacement Point Forecasting, Interval Forecasting and Probabilistic Forecasting

In addition to the inherent evolution trend, landslide displacement contains strong fluctuation and randomness, the omni-directional landslide displacement prediction is more scientific than single point prediction or interval prediction. In this work, a newly hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM) and gaussian process regression (GPR), was proposed for point, interval and probabilistic prediction of landslide displacement. The proposed model includes two parts: (i) predicting the inherent evolution trend of landslide displacement by DES-VMD-LSTM; (ii) evaluating the uncertainty in the first prediction based on the GPR model. In the first part, DES is used to predict the trend displacement, VMD is used to extract the periodic and stochastic displacement from the residual displacement, and then LSTM is used to predict them. The triggering factors of periodic and stochastic displacement are screened by maximum information coefficient (MIC), and the screened factors are decomposed into low- and high-frequency components by VMD, to predict periodic and stochastic displacement respectively. The first cumulative displacement prediction results are achieved by adding the predicted trend, periodic and stochastic displacement. By setting the first predicted displacement as input and actual displacement as expected output, the point, interval and probability prediction of displacement are realized in GPR model. The plausibility of this method was validated firstly with the data from Bazimen (BZM) and Baishuihe (BSH) landslide in the Three Gorges Reservoir area. This model has potential capacity to realize deterministic prediction of displacement and exhibit uncertainty contained in displacement. A comparing study shows that this method has a high performance at point, interval and probability prediction of displacement.

An Integrated Approach for Displacement Prediction of Landslides Based on the Data Mining and VMD-FOA-BPNN Model

Landslide prediction is important for mitigating geohazards but is very challenging. The fluctuation of reservoir water level and rainfall are the main external triggering factors controlling the deformation of riverine landslides. In this paper, the Baishuihe landslide in the Three Gorges Reservoir area, which has a typical “step-like” behavior, is taken as the study case, and an integrated approach for landslide displacement prediction combining data mining and Variational Mode Decomposition, Fruit Fly Optimization Algorithm, Back Propagation Neural Network (VMD-FOA-BPNN) is proposed. Nine triggering factors including the reservoir level and rainfall are extracted. First, triggering factors and monthly velocity of the landslide are clustered into several categories by Two-step Clustering (TSC). Then, Apriori algorithm is used to mine the association rules between triggering factors and monthly velocity, and comprehensive contribution of each factor is calculated based on the data mining results. Next, the displacement of monitoring point ZG93 and triggering factors are decomposed by VMD based on the time series analysis of the landslide. Last, the trend term displacement is trained and predicted by the subsection functions, and FOA-BPNN models are used to train and predict the periodic and random term. The prediction results show that, compared with the current popular prediction models, the proposed model can effectively improve the prediction accuracy, which has high practicability and application value in the study of landslide displacement prediction.