scholarly journals Development of a Hybrid Method to Predict the Slope Surface Deformation Utilizing the Time Series and GA-Elman Models

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Gang Wang ◽  
Zheng Fang ◽  
Jiren Xie ◽  
Na Du
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Hybrid Method ◽  
Surface Deformation ◽  
Back Propagation ◽  
Periodic Component ◽  
Economic Losses ◽  
Support Vector ◽  
Slope Surface ◽  
Trend Component

A reliable prediction of the surface deformation of slopes is vital to better assess the fatalities and economic losses caused by landslides. Many prediction methods have been proposed to estimate the surface deformation of landslides with nonlinear characteristics. However, these methods have low accuracy and poor applicability. In this paper, a new hybrid method for surface deformation prediction was proposed, which was deduced from the Wavelet Analysis, Genetic Algorithm (GA), and Elman Algorithm. In this method, the slope surface deformation was decomposed into a trend component and a periodic component using the time series model, which were trained and predicted utilizing the GA-Elman model. The predicted slope surface deformation was the combination of the trend component and the periodic component. Then, the predicted results of slope surface deformation through GA-Elman were compared with the predicted results through Support Vector Machines (SVM), Extreme Learning Machine (ELM), Back Propagation (BP), and Genetic Algorithm-Back Propagation (GA-BP) models. The comparison was made with reference to the data retrieved from the on-site slopes and the laboratory tests. The results revealed that the proposed method highlighted reliability and could be used with higher accuracy to forecast the slope surface deformation in the process of landslides.

scholarly journals Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

2017 ◽  
Author(s):  
Tao Wen ◽  
Huiming Tang ◽  
Yankun Wang ◽  
Chengyuan Lin ◽  
Chengren Xiong
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Periodic Component ◽  
Economic Losses ◽  
Support Vector ◽  
Series Analysis ◽  
Trend Component ◽  
Geological Conditions ◽  
Component Displacement

Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall, and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with step-like deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

scholarly journals Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

2017 ◽  
Vol 17 (12) ◽  
pp. 2181-2198 ◽  
Author(s):  
Tao Wen ◽  
Huiming Tang ◽  
Yankun Wang ◽  
Chengyuan Lin ◽  
Chengren Xiong
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Periodic Component ◽  
Economic Losses ◽  
Support Vector ◽  
Series Analysis ◽  
Trend Component ◽  
Geological Conditions ◽  
Component Displacement

Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

APPLICATION OF GENETIC ALGORITHM-SUPPORT VECTOR MACHINE (GA-SVM) FOR DAMAGE IDENTIFICATION OF BRIDGE

2011 ◽  
Vol 10 (04) ◽  
pp. 383-397 ◽  
Author(s):  
HAN-BING LIU ◽  
YU-BO JIAO
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Damage Identification ◽  
Back Propagation ◽  
Support Vector ◽  
Model Parameters ◽  
Simply Supported ◽  
Relative Errors ◽  
Shape Ratio ◽  
Crossover And Mutation

A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ratio and frequency rate is used as the input variable. A numerical example for a simply supported bridge with five girders is provided to verify the feasibility of the method. Numerical simulation shows that the maximal relative errors of GA-SVM for the damage identification of single, two and three suspicious damaged elements is 1.84%. Meanwhile, comparative analyzes between GA-SVM and radical basis function (RBF), back propagation networks optimized by GA (GA-BP) were conducted, the maximal relative errors of RBF and GA-BP are 6.91% and 5.52%, respectively. It indicates that GA-SVM can assess the damage conditions with better accuracy.

scholarly journals Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Liu ◽  
Songyong Liu ◽  
Qianjin Dai ◽  
Xiao Yu ◽  
Daoxiang Teng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Operating Conditions ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Fault Detection And Identification ◽  
Vector Machines

Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

scholarly journals An Optimized Method of Wind Speed Prediction with Support Vector Machine and Genetic Algorithm

2021 ◽  
Vol 15 ◽  
pp. 212-217
Author(s):  
Guan-fa Li ◽  
Wen-sheng Zhu
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Power System ◽  
Prediction Models ◽  
Optimization Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

Due to the randomness of wind speed and direction, the output power of wind turbine also has randomness. After large-scale wind power integration, it will bring a lot of adverse effects on the power quality of the power system, and also bring difficulties to the formulation of power system dispatching plan. In order to improve the prediction accuracy, an optimized method of wind speed prediction with support vector machine and genetic algorithm is put forward. Compared with other optimization methods, the simulation results show that the optimized genetic algorithm not only has good convergence speed, but also can find more suitable parameters for data samples. When the data is updated according to time series, the optimization range of vaccine and parameters is adaptively adjusted and updated. Therefore, as a new optimization method, the optimization method has certain theoretical significance and practical application value, and can be applied to other time series prediction models.

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