Impact of Non-Landslide Sample Sampling Strategies and Model Selection on Landslide Susceptibility Mapping
This study investigated the influence of non-landslide sampling strategies on landslide susceptibility assessment (LSA) performance and explored approaches to minimizing uncertainty in model selection. Five non-landslide sampling strategies were evaluated using the random forest (RF) model to genera...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/4/2132 |
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| Summary: | This study investigated the influence of non-landslide sampling strategies on landslide susceptibility assessment (LSA) performance and explored approaches to minimizing uncertainty in model selection. Five non-landslide sampling strategies were evaluated using the random forest (RF) model to generate landslide susceptibility maps (LSMs) for each scenario. To assess the impact of these strategies, this study employed a receiver operating characteristic (ROC) curve, a confusion matrix, and various statistical indicators. Additionally, the mean susceptibility indices derived from the gradient boosting decision tree (GBDT), support vector machine (SVM), and RF models were analyzed to evaluate their effectiveness in reducing the uncertainty during model selection. The GBDT, SVM, and RF were selected for their ability to handle complex, nonlinear relationships in the data, superior generalization capability, effective mitigation of overfitting risks, high predictive performance, and robustness. The findings revealed that selecting non-landslide samples from slope units without landslides enhances accuracy and averaging across models mitigated the uncertainty associated with landslide susceptibility models. Furthermore, this study demonstrated that the non-landslide sample selection method significantly improved prediction accuracy, particularly when samples were drawn from very-low-susceptibility zones identified by pre-classified machine learning models. These results highlight the importance of refining sample selection strategies and integrating multiple machine learning models to improve the reliability and accuracy of landslide susceptibility assessments. This approach provides valuable insights for future research and practical applications in risk mitigation and disaster management by offering a more precise depiction of low-susceptibility areas, thereby reducing the occurrence of false positives in landslide prediction. |
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| ISSN: | 2076-3417 |