| Citation: |
JIA Yufei, WEI Wenhao, CHEN Wen, YANG Qingzhuo, SHENG Yifan, XU Guangli. Landslide susceptibility assessment based on the SOM-I-SVM model[J]. Hydrogeology & Engineering Geology, 2023, 50(3): 125-137. doi: 10.16030/j.cnki.issn.1000-3665.202206041
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Landslide susceptibility assessment based on the SOM-I-SVM model
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Abstract
When using machine learning models for landslide susceptibility evaluation, the non-landslide sample points are usually selected randomly outside the landslide influence area, leading to a certain error. To improve the accuracy of landslide susceptibility evaluation, this paper couples the self-organizing map (SOM) neural network, information (I) model, and support vector machine (SVM) model, and proposes a SOM-I-SVM model-based method of landslide susceptibility evaluation, comparing with K-means clustering to verify the reliability of this model. The Maojian District of the city of Shiyan is taken as an example, and seven factors of the distance from water system, slope, rainfall, distance from structure, relative height difference, distance from road, stratigraphic lithology are selected by correlation and importance analyses of environmental factors to establish a landslide susceptibility evaluation system. Based on these, the graded information values of each factor are calculated and used as input variables for landslide susceptibility evaluation. The SOM neural network and K-means clustering are used to select non-landslide samples, and the sample data set is substituted into the I-SVM model to predict landslide susceptibility. The prediction accuracies of the four models, SVM, I-SVM, KMeans-I-SVM and SOM-I-SVM, are compared, and the area under the ROC curve (AUC values) are 0.82, 0.88, 0.90 and 0.91, indicating that the SOM-I-SVM model can effectively improve the accuracy of landslide susceptibility prediction.
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Keywords:
- landslide; susceptibility assessment /
- information model /
- SVM /
- SOM
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References
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JIA Yufei, WEI Wenhao, CHEN Wen, YANG Qingzhuo, SHENG Yifan, XU Guangli. Landslide susceptibility assessment based on the SOM-I-SVM model[J]. Hydrogeology & Engineering Geology, 2023, 50(3): 125-137. doi: 10.16030/j.cnki.issn.1000-3665.202206041
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Figure 1.
Location of the Maojian District
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Figure 2.
SOM Neural Network
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Figure 3.
Support vector machine model
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Figure 4.
Process of the SOM-I-SVM model
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Figure 5.
Index factors of landslide hazard susceptibility evaluation in the study area
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Figure 6.
Correlation chart of impact factors
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Figure 7.
Importance chart of evaluation factors
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Figure 8.
Zoning map of landslide susceptibility based on the SVM model
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Figure 9.
Zoning map of landslide susceptibility based on the I-SVM model
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Figure 10.
Landslide susceptibility zoning and sample selection of the KMeans model
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Figure 11.
Landslide susceptibility zoning and sample selection of the SOM neural network
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Figure 12.
Zoning map of landslide susceptibility based on the KMeans-I-SVM model
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Figure 13.
Zoning map of landslide susceptibility based on the SOM-I-SVM model
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Figure 14.
Number of historical landslide sites of each vulnerability level
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Figure 15.
ROC curves of the four used models