A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network
Landslide susceptibility mapping (LSM) plays a crucial role in assessing geological risks. The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters. To explore rainfall-induced LSM, this study pr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S167477552400355X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Landslide susceptibility mapping (LSM) plays a crucial role in assessing geological risks. The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters. To explore rainfall-induced LSM, this study proposes a hybrid model that combines the physically-based probabilistic model (PPM) with convolutional neural network (CNN). The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure (POF) considering the slope stability mechanism under rainfall conditions. This significantly characterizes the variation of POF caused by parameter uncertainties. CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence. OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides. The proposed model comprehensively considers physical mechanics when selecting non-landslide samples, effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting. The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy, with an area under the curve (AUC) value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province, China compared with the individual CNN model (AUC = 0.61) and the PPM (AUC = 0.74). This model can also consider the statistical correlation and non-normal probability distributions of model parameters. These results offer practical guidance for future research on rainfall-induced LSM at the regional scale. |
|---|---|
| ISSN: | 1674-7755 |