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

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.

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

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
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.

scholarly journals A Landslide Displacement Prediction Method with Iteration-Based Combined Strategy

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.

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

2016 ◽  
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.

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

2021 ◽  
Author(s):  
Fasheng Miao ◽  
Xiaoxu Xie ◽  
Yiping Wu ◽  
Linwei Li ◽  
Weiwei Zhan
Keyword(s):  
Data Mining ◽  
Prediction Models ◽  
Integrated Approach ◽  
Three Gorges Reservoir Area ◽  
Fruit Fly Optimization Algorithm ◽  
Reservoir Water ◽  
Displacement Prediction ◽  
Triggering Factors ◽  
Landslide Displacement Prediction ◽  
Combining Data

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.

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

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.

Day-ahead Load Probabilistic Forecasting Based on Space-time Correction

2020 ◽  
Vol 13 ◽  
Author(s):  
Fei Jin ◽  
Xiaoliang Liu ◽  
Fangfang Xing ◽  
Guoqiang Wen ◽  
Shuangkun Wang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Northern China ◽  
Space Time ◽  
Load Prediction ◽  
Probabilistic Forecasting ◽  
Residual Distribution ◽  
Time And Space ◽  
Probability Prediction ◽  
Time Correction ◽  
Interval Coverage

Background : The day-ahead load forecasting is an essential guideline for power generating, and it is of considerable significance in power dispatch. Objective: Most of the existing load probability prediction methods use historical data to predict a single area, and rarely use the correlation of load time and space to improve the accuracy of load prediction. Methods: This paper presents a method for day-ahead load probability prediction based on space-time correction. Firstly, the kernel density estimation (KDE) is employed to model the prediction error of the long short-term memory (LSTM) model, and the residual distribution is obtained. Then the correlation value is used to modify the time and space dimensions of the test set's partial period prediction values. Results: The experiment selected three years of load data in 10 areas of a city in northern China. The MAPE of the two modified models on their respective test sets can be reduced by an average of 10.2% and 6.1% compared to previous results. The interval coverage of the probability prediction can be increased by an average of 4.2% and 1.8% than before. Conclusion: The test results show that the proposed correction schemes are feasible.

scholarly journals Probability of Roadside Accidents for Curved Sections on Highways

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Guozhu Cheng ◽  
Rui Cheng ◽  
Yulong Pei ◽  
Liang Xu
Keyword(s):  
Risk Factors ◽  
Decision Rules ◽  
Significant Risk ◽  
Vehicle Speed ◽  
Probabilistic Prediction ◽  
Interaction Detection ◽  
Probability Prediction ◽  
Chi Squared ◽  
Longitudinal Slope ◽  
Curve Radius

To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.

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