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Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges

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Publicado en:Remote Sensing vol. 17, no. 13 (2025), p. 2280-2318
Autor principal: Ye Chenzuo
Otros Autores: Wu, Hao, Oguchi Takashi, Tang, Yuting, Pei Xiangjun, Wu, Yufeng
Publicado:
MDPI AG
Materias:
Deep learning
Trends
Susceptibility
Landslides
Remote sensing
Mountainous areas
Hydrology
Hazard assessment
Data integration
Machine learning
Mountain regions
Stability analysis
Geology
Data analysis
Hazard mitigation
Spatial data
Landslides & mudslides
Failure analysis
Predictions
Neural networks
Regions
Geological hazards
Earthquakes
Disasters
Physical properties
Algorithms
Real time
Multisensor fusion
Acceso en línea:Citation/Abstract
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Resumen:Susceptibility assessment is a crucial task for mitigating landslide hazards. It includes displacement prediction, stability analysis, and location prediction for individual hillslopes or regional mountainous areas. Physically based models can assess landslide susceptibility with limited datasets by inputting physical parameters, albeit with some uncertainties. In contrast, data-driven models, primarily developed using machine learning and statistical algorithms, often provide acceptable predictive accuracy in assessing landslide susceptibility. They generally serve as practical tools for prediction but lack transparency and scientific interpretability. This review critically analyzes the strengths, limitations, and application scenarios of each model type, with a focus on recent advancements, practical applications, and challenges encountered. Furthermore, potential integration strategies are discussed to address the limitations of each approach, including hybrid models that combine the interpretability of physically based models with the predictive power of data-driven models. Finally, we suggest future research directions to improve landslide susceptibility assessments, such as enhancing model interpretability, incorporating real-time monitoring data, enhancing cross-regional transferability, and leveraging advancements in remote sensing, spatial data analytics, and multi-source data fusion.
ISSN:2072-4292
DOI:10.3390/rs17132280
Fuente:Advanced Technologies & Aerospace Database