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

Abstract 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.

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

Sensors ◽

10.3390/s22020481 ◽

2022 ◽

Vol 22 (2) ◽

pp. 481

Author(s):

Fasheng Miao ◽

Xiaoxu Xie ◽

Yiping Wu ◽

Fancheng Zhao

Keyword(s):

Data Mining ◽

Water Levels ◽

Three Gorges Reservoir Area ◽

Data Mining Algorithm ◽

Fruit Fly Optimization Algorithm ◽

Reservoir Water ◽

Triggering Factors ◽

Prediction And Prevention ◽

Landslide Displacement Prediction ◽

Baishuihe Landslide

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.

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A Landslide Displacement Prediction Method with Iteration-Based Combined Strategy

Mathematical Problems in Engineering ◽

10.1155/2021/6692503 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

L. Li ◽

S. X. Zhang ◽

Y. Qiang ◽

Z. Zheng ◽

S. H. Li ◽

...

Keyword(s):

Engineering Application ◽

Prediction Method ◽

Three Gorges Reservoir Area ◽

Reservoir Water ◽

Reservoir Water Level ◽

Displacement Prediction ◽

The Three Gorges ◽

Landslide Displacement Prediction ◽

Grey Models ◽

Combined Strategy

Predicting landslide displacement is of great significance in geotechnical engineering. An iteration-based combined prediction method was proposed for predicting the landslide displacement in this paper. Firstly, the landslide displacement was predicted by 10 latest multivariable grey models, and then the final landslide displacement prediction value was obtained through an iteration-based combined strategy. Concurrently, the reliability of the quadratic programming-based combined prediction method (QPCPM) and the iteration-based combined prediction method (ICPM) was rigorously proved in this paper. In addition, the inapplicability conditions of the optimal weight-based combined prediction method (OWCPM) were pointed out. ICPM could be regarded as a simplified version of QPCPM. The Bazimen and Baishuihe landslides in the Three Gorges Reservoir area of China were used as numerical examples to elaborate the performance of ICPM. This paper also demonstrated the reliability of ICPM by considering the effects of rainfall and reservoir water level on landslide displacement. Overall, ICPM features in simple and easy calculation and has rosy engineering application prospects.

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Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China

Geofluids ◽

10.1155/2019/8985325 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Yankun Wang ◽

Huiming Tang ◽

Tao Wen ◽

Junwei Ma ◽

Zongxing Zou ◽

...

Keyword(s):

Three Gorges Reservoir ◽

Three Gorges Reservoir Area ◽

Support Vector ◽

Three Gorges ◽

Noise Variance ◽

Displacement Prediction ◽

Reservoir Area ◽

The Three Gorges Reservoir ◽

The Three Gorges ◽

Landslide Displacement Prediction

Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement.

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Adaptive Hybrid Machine Learning Model For Forecasting The Step-Like Displacements of Reservoir Colluvial Landslides: A Case Study in The Three Gorges Reservoir Area, China

10.21203/rs.3.rs-217782/v1 ◽

2021 ◽

Author(s):

Li Linwei ◽

Yiping Wu ◽

Miao Fasheng ◽

Xue Yang ◽

Huang Yepiao

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Warning System ◽

Learning Model ◽

Model Complexity ◽

Three Gorges Reservoir Area ◽

Gray Wolf ◽

Displacement Prediction ◽

Machine Learning Model ◽

Hybrid Machine

Abstract Constructing an accurate and stable displacement prediction model is essential to build a capable early warning system for landslide disasters. To overcome the drawbacks of previous displacement prediction models for step-like landslides, such as the incomplete or excessive decompositions of cumulative displacements and input factors and the redundancy or lack of input factors, we propose an adaptive hybrid machine learning model. This model is composed of three parts. First, candidate factors are proposed based on the macroscopic deformation response of landslides. Then, the landslide displacement and its candidate factors are adaptively decomposed into different displacement and factor components by applying optimized variational mode decomposition (OVMD). Second, in the gray wolf optimizer-based kernel extreme learning machine (GWO-KELM) model, the global sensitivity analysis (GSA) of the prediction results of different displacement components to each decomposed factor is analyzed based on the PAWN method. Then, the decomposed factors are reduced according to the GSA results. Third, based on the reduced factors, the optimal GWO-KELM models of the different displacement components are established to predict the displacement. Taking the Baishuihe landslide as an example, we used the raw data of three representative monitoring sites from June 2006 to December 2016 to verify the validity, accuracy, and stability of the model. The results indicate that the proposed hybrid model can effectively determine the displacement decomposition parameters. In addition, this model performed well over a three-year forecast with low model complexity.

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Rainfall feature extraction using cluster analysis and its application on displacement prediction for a cleavage-parallel landslide in the Three-Gorges Reservoir area

10.5194/nhess-2015-320 ◽

2016 ◽

Cited By ~ 2

Author(s):

Y. Liu ◽

L. Liu

Keyword(s):

Cluster Analysis ◽

Feature Extraction ◽

Back Propagation ◽

Daily Rainfall ◽

Rainfall Data ◽

Data Availability ◽

Three Gorges Reservoir Area ◽

Displacement Prediction ◽

Deformation And Failure ◽

Landslide Displacement Prediction

Abstract. Rainfall is one of the most important factors controlling landslide deformation and failure. State-of-art rainfall data collection is a common practice in modern landslide research world-wide. Nevertheless, in spite of the availability of high-accuracy rainfall data, it is not a trivial process to diligently incorporate rainfall data in predicting landslide stability due to large quantity, tremendous variety, and wealth multiplicity of rainfall data. Up to date, most of the pre-process procedure of rainfall data only use mean value, maxima and minima to characterize the rainfall feature. This practice significantly overlooks many important and intrinsic features contained in the rainfall data. In this paper, we employ cluster analysis (CA)-based feature analysis to rainfall data for rainfall feature extraction. This method effectively extracts the most significant features of a rainfall sequence and greatly reduced rainfall data quantities. Meanwhile it also improves rainfall data availability. For showing the efficiency of using the CA characterized rainfall data input, we present three schemes to input rainfall data in back propagation (BP) neural network to forecast landslide displacement. These three schemes are: the original daily rainfall, monthly rainfall, and CA extracted rainfall features. Based on the examination of the root mean square error (RMSE) of the landslide displacement prediction, it is clear that using the CA extracted rainfall features input significantly improve the ability of accurate landslide prediction.

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Prediction of Landslide Displacement Based on Kernel Principal Component Analysis and Neural Network-Markov Chain

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.1512 ◽

2013 ◽

Vol 726-731 ◽

pp. 1512-1520

Author(s):

Yan Nan Zhao ◽

Rui Qing Niu ◽

Jiong Li ◽

Ling Peng ◽

Yi Wang

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Markov Chain ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Three Gorges Reservoir Area ◽

Reservoir Water ◽

Displacement Prediction ◽

Inducing Factors

Taking Baishuihe landslide in the Three Gorges Reservoir Area as an example, the features of landslide deformation influencing factors such as rainfall, reservoir water and groundwater were extracted using kernel principal component analysis method, which were used to build the BP neural network displacement prediction model and then to output the fitted values. Secondly, Markov chain (MC) was used to optimize the prediction error through analysis of the error range between fitted values and measured values, timing displacement state division and state transfer probability matrix calculation. Finally, the BP-MC model was constructed, using which dynamically displacement prediction was achieved. The fitted and predicted results show that this model can reflect the relationship between the inducing factors and landslide displacement and can effectively improve the prediction accuracy of landslide displacement.

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

10.21203/rs.3.rs-839771/v1 ◽

2021 ◽

Author(s):

Hong Wang ◽

Guangyu Long ◽

Jianxing Liao ◽

Yan Xu ◽

Yan Lv

Keyword(s):

Short Term Memory ◽

Single Point ◽

Hybrid Approach ◽

Three Gorges Reservoir Area ◽

Probabilistic Prediction ◽

Displacement Prediction ◽

Maximum Information ◽

Landslide Displacement Prediction ◽

Probability Prediction ◽

Evolution Trend

Abstract 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.

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Landslide Displacement Prediction Based on Variational Mode Decomposition and MIC-GWO-LSTM Model

10.21203/rs.3.rs-507356/v1 ◽

2021 ◽

Author(s):

Taorui Zeng ◽

Hongwei Jiang ◽

Qingli Liu ◽

Kunlong Yin

Keyword(s):

Prediction Model ◽

Three Gorges Reservoir ◽

Three Gorges Reservoir Area ◽

Soil Conditions ◽

Three Gorges ◽

Displacement Prediction ◽

Reservoir Area ◽

The Three Gorges Reservoir ◽

The Three Gorges ◽

Landslide Displacement Prediction

Abstract Landslide displacement prediction is essential to establish the early warning system (EWS). According to the dynamic characteristics of landslide evolution and the shortcomings of the traditional static prediction model, a dynamic prediction model of landslide displacement based on long short-term memory (LSTM) neural networks was proposed. Meanwhile, the Variational modal decomposition (VMD) theory was used to decompose the cumulative displacement and triggering factors, which not only give clear physical meaning to each displacement subsequence, but also closely connect the rock and soil conditions with the influence of external factors. Besides, the maximum information coefficient (MIC) was used to sort the redundant features. The LSTM is a dynamic model that can remember historical information and apply it to the current output. The hyperparameters of the LSTM model was optimized by the Grey wolf optimizer (GWO), and the dynamic one-step prediction was carried out for each displacement. All the predicted values were superimposed to complete the displacement prediction based on the time series model. The Tangjiao landslide in the Three Gorges Reservoir area (TGRA), China, was taken as a case study. The displacement data of monitoring sites GPS06 had step-like characteristics. Measured data from March 2007 to December 2016 were selected for analysis. The results indicate that the root mean square error (RMSE) of the test set and validation set are 23.240 mm and 64.714 mm, respectively, and the coefficient of determination (R2) are 0.997 and 0.971, respectively. This model provides a new idea and exploration for the displacement prediction of step-like characteristics landslide in the Three Gorges Reservoir area.

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