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

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 c...

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Main Authors: Ding Xia, Huiming Tang, Sixuan Sun, Chunyan Tang, Bocheng Zhang
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/14/11/2707
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author Ding Xia
Huiming Tang
Sixuan Sun
Chunyan Tang
Bocheng Zhang
author_facet Ding Xia
Huiming Tang
Sixuan Sun
Chunyan Tang
Bocheng Zhang
author_sort Ding Xia
collection DOAJ
description 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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spelling doaj.art-c35da1ffb6f747e7adbb7a573ac77ad62023-11-23T14:46:09ZengMDPI AGRemote Sensing2072-42922022-06-011411270710.3390/rs14112707Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector ClassificationDing Xia0Huiming Tang1Sixuan Sun2Chunyan Tang3Bocheng Zhang4Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaA 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.https://www.mdpi.com/2072-4292/14/11/2707landslide susceptibility mappingThree Gorges Reservoir areasupport vector classificationGCO-SVC
spellingShingle Ding Xia
Huiming Tang
Sixuan Sun
Chunyan Tang
Bocheng Zhang
Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
Remote Sensing
landslide susceptibility mapping
Three Gorges Reservoir area
support vector classification
GCO-SVC
title Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
title_full Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
title_fullStr Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
title_full_unstemmed Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
title_short Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
title_sort landslide susceptibility mapping based on the germinal center optimization algorithm and support vector classification
topic landslide susceptibility mapping
Three Gorges Reservoir area
support vector classification
GCO-SVC
url https://www.mdpi.com/2072-4292/14/11/2707
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AT sixuansun landslidesusceptibilitymappingbasedonthegerminalcenteroptimizationalgorithmandsupportvectorclassification
AT chunyantang landslidesusceptibilitymappingbasedonthegerminalcenteroptimizationalgorithmandsupportvectorclassification
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