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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MDPI AG
2022-06-01
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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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issn | 2072-4292 |
language | English |
last_indexed | 2024-03-10T00:54:11Z |
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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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