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!
|
| _version_ | 1849335631986032640 |
|---|---|
| author | Hong-Zhi Cui Bin Tong Tao Wang Jie Dou Jian Ji |
| author_facet | Hong-Zhi Cui Bin Tong Tao Wang Jie Dou Jian Ji |
| author_sort | Hong-Zhi Cui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d5e9bd9ad1c94a93be3c80b4f1f99a12 |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-d5e9bd9ad1c94a93be3c80b4f1f99a122025-08-20T03:45:11ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552025-08-011784933495110.1016/j.jrmge.2024.08.005A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural networkHong-Zhi Cui0Bin Tong1Tao Wang2Jie Dou3Jian Ji4Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, 210024, China; Division of Geotechnical Engineering and Geosciences, Department of Civil and Environmental Engineering, Universitat Politecnica de Catalunya, Barcelona, 08034, SpainKey Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, 210024, ChinaKey Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, 210024, ChinaBadong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan, 430074, ChinaKey Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, 210024, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S167477552400355XRainfall landslidesLandslide susceptibility mappingHybrid modelPhysically-based modelConvolution neural network (CNN)Probability of failure (POF) |
| spellingShingle | Hong-Zhi Cui Bin Tong Tao Wang Jie Dou Jian Ji A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network Journal of Rock Mechanics and Geotechnical Engineering Rainfall landslides Landslide susceptibility mapping Hybrid model Physically-based model Convolution neural network (CNN) Probability of failure (POF) |
| title | A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network |
| title_full | A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network |
| title_fullStr | A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network |
| title_full_unstemmed | A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network |
| title_short | A hybrid data-driven approach for rainfall-induced landslide susceptibility mapping: Physically-based probabilistic model with convolutional neural network |
| title_sort | hybrid data driven approach for rainfall induced landslide susceptibility mapping physically based probabilistic model with convolutional neural network |
| topic | Rainfall landslides Landslide susceptibility mapping Hybrid model Physically-based model Convolution neural network (CNN) Probability of failure (POF) |
| url | http://www.sciencedirect.com/science/article/pii/S167477552400355X |
| work_keys_str_mv | AT hongzhicui ahybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT bintong ahybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT taowang ahybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT jiedou ahybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT jianji ahybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT hongzhicui hybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT bintong hybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT taowang hybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT jiedou hybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork AT jianji hybriddatadrivenapproachforrainfallinducedlandslidesusceptibilitymappingphysicallybasedprobabilisticmodelwithconvolutionalneuralnetwork |