Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges
Abstract
1. Introduction
2. Construction and Analysis of the Literature Database
2.1. Information on Articles
2.2. Overview of Whole Landslide Susceptibility Studies
2.3. Overview of Studies Using Physically Based Models
2.4. Overview of Landslide Susceptibility Studies Related to Data-Driven Models
3. Physically Based Models for Landslide Susceptibility Assessment
3.1. Principles for Physically Based Models
3.2. Selection of Input Parameters in Physically Based Models
3.3. Classification of Physically Based Models by Processes
3.3.1. Static Stability Models
3.3.2. Dynamic Displacement Prediction Models
3.3.3. Water–Soil Coupled Models
3.3.4. Climate-Driven Models
3.4. Classification of Physically Based Models by Spatial Scales
3.5. Classification of Physically Based Models by Computational Methods
3.5.1. Analytical Models
3.5.2. Numerical Models
3.5.3. Experimental Models
3.6. Classification of Physically Based Models by Uncertainty Handling Methods
3.6.1. Deterministic Models
3.6.2. Probabilistic Models
3.6.3. Scenario-Based Models
3.7. Strengths and Limitations of Physically Based Models
4. Data-Driven Models for Landslide Susceptibility Assessment
4.1. Principles for Data-Driven Models
4.2. Selection of Input Parameters in Data-Driven Models
4.3. Classification of Statistical Models for Landslide Susceptibility
4.3.1. Logistic Regression
4.3.2. Frequency Ratio
4.3.3. Weight of Evidence
4.4. Classification of Traditional Machine Learning Models for Landslide Susceptibility
4.4.1. Support Vector Machines
4.4.2. Random Forest
4.5. Classification of Deep Learning Models for Landslide Susceptibility
4.5.1. Multilayer Perceptron
4.5.2. Convolutional Neural Network
4.5.3. Transformer-Based Model
4.6. Evaluation Metrics and Methods
4.7. Strengths and Limitations of Data-Driven Models
5. Discussion and Prospect for Future Research
5.1. Multi-Level Integration Framework for Landslide Susceptibility Prediction
5.2. Multi-Source and Multi-Scale Data Fusion for Physically Based and Data-Driven Models
5.3. Parameter Optimization and Uncertainty Quantification in Physically Based and Data-Driven Models
5.4. Enhancing Regional Transferability of Physically Based and Data-Driven Models
5.5. Improving Interpretability in Physically Based and Data-Driven Models
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Sub-Category | Description |
---|---|---|
A. Bibliographic Information | A1 | Article ID/record number (unique identifier from the database) |
A2 | Article title | |
A3 | Author(s) | |
A4 | Publication year | |
A5 | Journal name | |
A6 | Citation count (optional) | |
A7 | Abstract | |
B. Regional and Geographic Information | B1 | Continent (e.g., Asia, Europe, North America, South America) |
B2 | Country (e.g., China, Italy, USA, Japan) | |
B3 | Specific region (e.g., Himalayas, Alps, Yangtze River Basin) | |
B4 | Coordinates (latitude/longitude, if available) | |
C. Landslide Characteristics and Scenario | C1 | Triggering factor (e.g., rainfall, earthquake, human activities, reservoir discharge) |
C2 | Study scale (e.g., local, regional, national, global) | |
C3 | Data availability type (e.g., remote sensing, field monitoring, historical database) | |
D. Methodological Information | D1 | Model type (physically based, data-driven, hybrid) |
D1a | Physically based models (e.g., SHALSTAB, SINMAP, TRIGRS, infinite slope model) | |
D1b | Data-driven models (e.g., LR, RF, ANN, SVM, CNN, Transformer) | |
D1c | Hybrid models (e.g., physically informed ML, coupled models) | |
D2 | Specific algorithm or model used (e.g., RF + SHALSTAB, TRIGRS–CNN, knowledge-based XGBoost) |
First Author | Year | Citations | Article Title |
---|---|---|---|
Goetz, Jason N. (Canada) [39] | 2011 | 209 | Integrating physical and empirical landslide susceptibility models using generalized additive models |
Salciarini, Diana (Italy) [40] | 2006 | 208 | Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy |
Cervi, Federico (Italy) [41] | 2010 | 129 | Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy) |
Gorsevski, Pece V. (North Macedonia) [42] | 2006 | 127 | Spatially and temporally distributed modeling of landslide susceptibility |
Ciurleo, Mariantonietta (Italy) [43] | 2017 | 96 | A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils |
First Author | Year | Citations | Article Title |
---|---|---|---|
Reichenbach, Paola (Italy) [38] | 2018 | 1322 | A review of statistically based landslide susceptibility models |
Pradhan, Biswajeet (India) [44] | 2013 | 998 | A comparative study on the predictive ability of the decision tree, support vector machine, and neuro-fuzzy models in landslide susceptibility mapping using GIS |
Dieu Tien Bui (Vietnam) [45] | 2016 | 995 | Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree |
Pradhan, Biswajeet (India) [46] | 2010 | 749 | Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling |
Chen, Wei (China) [32] | 2017 | 682 | A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility |
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Ye, C.; Wu, H.; Oguchi, T.; Tang, Y.; Pei, X.; Wu, Y. Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges. Remote Sens. 2025, 17, 2280. https://doi.org/10.3390/rs17132280
Ye C, Wu H, Oguchi T, Tang Y, Pei X, Wu Y. Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges. Remote Sensing. 2025; 17(13):2280. https://doi.org/10.3390/rs17132280
Chicago/Turabian StyleYe, Chenzuo, Hao Wu, Takashi Oguchi, Yuting Tang, Xiangjun Pei, and Yufeng Wu. 2025. "Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges" Remote Sensing 17, no. 13: 2280. https://doi.org/10.3390/rs17132280
APA StyleYe, C., Wu, H., Oguchi, T., Tang, Y., Pei, X., & Wu, Y. (2025). Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges. Remote Sensing, 17(13), 2280. https://doi.org/10.3390/rs17132280