Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model

Liu, Limin; Cheng, Shixin; Wen, Zhen

doi:10.1007/s11629-020-6396-5

Cited by 21 publications

(10 citation statements)

References 66 publications

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“…SVR is an application model for SVM to regression problems, which performs classification and regression tasks by mapping data to a high-dimensional space using kernel functions and by finding an optimal hyperplane in the feature space. SVR has been successfully applied to system identification, nonlinear system prediction and so on, and has achieved good results [38]. SVR creates an interval band on both sides of the linear function (Fig.…”

Section: ) Mlp Modelmentioning

confidence: 99%

“…The objective of this study is to construct the blending and stacking ensemble learning methods for TSSM based on CNN, MLP, SVR and RF single classifiers. CNN is an outstanding deep learning models for susceptibility mapping [34], MLP, SVR and RF are popular machine learning model for susceptibility mapping [37], [38], [39], CNN, MLP, SVR and RF are selected as base classifiers, and it is bound to get better susceptibility mapping results when constructing ensemble learning models. Among them, CNN, MLP and SVR are used as the first classifier and RF is used as the second classifier.…”

Section: A Evaluation Of the Ensemble Learning Modelsmentioning

confidence: 99%

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Thaw Slump Susceptibility Mapping Based on Sample Optimization and Ensemble Learning Techniques in Qinghai-Tibet Railway Corridor

He,

Huo,

Gao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Thaw slump susceptibility mapping (TSSM) of Qinghai-Tibet Railway corridor (QTRC) is the prerequisite and basis for disaster assessment and prevention of permafrost projects. The objective of this study is to construct ensemble learning models based on single classifier models to generate the TSSM of the QTRC, compare and verify the performance of the models, and further explore the relationship between the high susceptibility area and environmental factors of the QTRC. The collinearity analysis was carried out by selecting 14 thaw slump conditioning factors (TSCFs). We used the balance bagging method for sample optimization, and the data set was divided into 70% training set and 30% verification set. Convolutional neural network (CNN), multilayer perceptron (MLP), support vector regression (SVR), random forest (RF) single classifiers were selected to construct blending and stacking ensemble learning models for the TSSM. The results showed that there was no collinearity among the 14 TSCFS. The comparison of model performance revealed that all models had good performance, but the constructed stacking and blending ensemble learning models had stable performance and high prediction accuracy for TSSM. The stacking ensemble learning model had the best effect, and the area under curve (AUC) value of receiver operating characteristic (ROC) curve reached 0.9607. It showed that the generated TSSM of QTRC based on stacking ensemble learning model had the highest reliability. The QTRC has local areas with high thaw slump susceptibility, mainly concentrated in the permafrost areas with high altitude, high slope, adjacent faults, sparse vegetation, ice and snow and the more cumulative precipitation.

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Section: ) Mlp Modelmentioning

confidence: 99%

Section: A Evaluation Of the Ensemble Learning Modelsmentioning

confidence: 99%

Thaw Slump Susceptibility Mapping Based on Sample Optimization and Ensemble Learning Techniques in Qinghai-Tibet Railway Corridor

He,

Huo,

Gao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This paper takes the parameter combination of the Elman neural network as training goal, the initial predictor was generated after Eq (19). The predictor was used as the gray wolf individual, to obtain the initial population.…”

Section: Sgwo-elman Modelmentioning

confidence: 99%

“…The experimental results proved that WOA-Elman has good engineering utility the porosity prediction. In addition, WOA-Elman also played an important role in weather prediction [18] and landslide probability prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

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Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

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“…Xu Yunjuan [6] et al conducted Lasso dimensionality reduction of principal components based on variable clustering, and the accuracy of variable selection was improved. Li Yajuan [7] solved the functional linear regression model by using functional principal component estimation and used Lasso to select the characteristic function to predict the return rate. Song Xiaofeng [8] optimized the model and accurately predicted the air quality based on ridge regression.…”

Section: Introductionmentioning

confidence: 99%

Time series regression based on Bayesian model averaging and principal component analysis

2023

ACSS

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This paper proposed an adaptive prediction model for high-dimensional time series data based on model averaging method and principal component analysis. Specifically, this paper considers the case where the response variable is a scalar and the predictor variable is a time series. Firstly, the high-dimensional time series data is extracted information by principal component analysis. Secondly, the Bayesian model averaging method is used to perform the forecast task based on the principal component projection matrix. The proposed method can effectively deal with the unsupervised nature of PCA and avoid the problem of selecting the number of PCA. It is demonstrated that the proposed method is competitive compared with the lasso regression and the ridge regression by real data analyses.

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Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model

Cited by 21 publications

References 66 publications

Thaw Slump Susceptibility Mapping Based on Sample Optimization and Ensemble Learning Techniques in Qinghai-Tibet Railway Corridor

Thaw Slump Susceptibility Mapping Based on Sample Optimization and Ensemble Learning Techniques in Qinghai-Tibet Railway Corridor

Improved GWO and its application in parameter optimization of Elman neural network

Time series regression based on Bayesian model averaging and principal component analysis

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