Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification

Abstract

A landslide susceptibility model based on a metaheuristic optimization algorithm (germinal center optimization (GCO)) and support vector classification (SVC) is proposed and applied to landslide susceptibility mapping in the Three Gorges Reservoir area in this paper. The proposed GCO-SVC model was constructed via the following steps: First, data on 11 influencing factors and 292 landslide polygons were collected to establish the spatial database. Then, after the influencing factors were subjected to multicollinearity analysis, the data were randomly divided into training and testing sets at a ratio of 7:3. Next, the SVC model with 5-fold cross-validation was optimized by hyperparameter space search using GCO to obtain the optimal hyperparameters, and then the best model was constructed based on the optimal hyperparameters and training set. Finally, the best model acquired by GCO-SVC was applied for landslide susceptibility mapping (LSM), and its performance was compared with that of 6 popular models. The proposed GCO-SVC model achieved better performance (0.9425) than the genetic algorithm support vector classification (GA-SVC; 0.9371), grid search optimized support vector classification (GRID-SVC; 0.9198), random forest (RF; 0.9085), artificial neural network (ANN; 0.9075), K-nearest neighbor (KNN; 0.8976), and decision tree (DT; 0.8914) models in terms of the area under the receiver operating characteristic curve (AUC), and the trends of the other metrics were consistent with that of the AUC. Therefore, the proposed GCO-SVC model has some advantages in LSM and may be worth promoting for wide use.

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Postearthquake Casualty Prediction Based on Heatmaps and Wavelet Supporting Vector Machines

“…e kernel function in an SVM is used to solve for the inner product in the high-dimensional space and minimize the nonlinear classification error. At present, the commonly used kernel functions include the radial basis kernel function, Fourier kernel function, sigmoid kernel function, polynomial kernel function, linear kernel function, and others [27,28]. ese kernel functions can effectively reduce the prediction error, but the result is still not ideal in some cases.…”

Experimental study on the mechanical properties and failure modes of BFRP bar anchor systems under static tension loading

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