Abstract
Conducting a precise landslide susceptibility assessment at the slope scale is challenging due to complex parameters and limited historical hazards data. This paper proposes a susceptibility assessment model based on the frequency ratio (FR) coupled with multiple regression analysis to solve these problems. Assessment factors were identified through field surveys and remote sensing interpretation. We utilized multiple methods, such as logistic regression (LR), partial least squares regression (PLSR), ridge regression (RR), stepwise regression (SR), and discriminant analysis (DA), and established five coupled models (FR-LR, FR-PLSR, FR-RR, FR-SR, and FR-DA). The reliability of the susceptibility assessment results was systematically verified. The results showed the following: (1) for the distribution of the susceptibility index, FR-PLSR, FR-RR, and FR-DA are close to the standard normal distribution; (2) for the prediction accuracy, the AUC indexes of the five models are relatively large, ranging from 0.86 to 0.87; and (3) for the susceptibility zoning reliability, FR-PLSR and FR-RR showed better performance, with a relatively smaller area proportion and larger susceptibility intensity for high susceptibility regions. Finally, the coupled models FR-PLSR and FR-RR were recommended. The areas of slope units categorized as high-, medium-, low-, and extra low-susceptibility are 16.14 km2, 73.85 km2, 98.96 km2, and 39.49 km2, respectively, accounting for 7.06%, 32.33%, 43.32%, and 17.29% of the study area. This work enriches the theoretical understanding of the susceptibility assessment models with limited historical hazards data and provides an effective coupled model for high-accuracy landslide assessment and prevention at the slope scale.
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This research was supported by the National Natural Science Foundation of China (No. 42172309).
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Sun, J., Yan, T., Hu, J. et al. Slope-scale landslide susceptibility assessment based on coupled models of frequency ratio and multiple regression analysis with limited historical hazards data. Nat Hazards 120, 1–23 (2024). https://doi.org/10.1007/s11069-023-06176-7
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DOI: https://doi.org/10.1007/s11069-023-06176-7