Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges
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| Udgivet i: | Remote Sensing vol. 17, no. 13 (2025), p. 2280-2318 |
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| Andre forfattere: | , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3229156903 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17132280 |2 doi | |
| 035 | |a 3229156903 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Ye Chenzuo |u Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-0882, Japan; yechenzuo@csis.u-tokyo.ac.jp (C.Y.); oguchi@csis.u-tokyo.ac.jp (T.O.); tang@ms.k.u-tokyo.ac.jp (Y.T.); | |
| 245 | 1 | |a Physically Based and Data-Driven Models for Landslide Susceptibility Assessment: Principles, Applications, and Challenges | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Deep learning | ||
| 653 | |a Trends | ||
| 653 | |a Susceptibility | ||
| 653 | |a Landslides | ||
| 653 | |a Remote sensing | ||
| 653 | |a Mountainous areas | ||
| 653 | |a Hydrology | ||
| 653 | |a Hazard assessment | ||
| 653 | |a Data integration | ||
| 653 | |a Machine learning | ||
| 653 | |a Mountain regions | ||
| 653 | |a Stability analysis | ||
| 653 | |a Geology | ||
| 653 | |a Data analysis | ||
| 653 | |a Hazard mitigation | ||
| 653 | |a Spatial data | ||
| 653 | |a Landslides & mudslides | ||
| 653 | |a Failure analysis | ||
| 653 | |a Predictions | ||
| 653 | |a Neural networks | ||
| 653 | |a Regions | ||
| 653 | |a Geological hazards | ||
| 653 | |a Earthquakes | ||
| 653 | |a Disasters | ||
| 653 | |a Physical properties | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Multisensor fusion | ||
| 700 | 1 | |a Wu, Hao |u School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China | |
| 700 | 1 | |a Oguchi Takashi |u Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-0882, Japan; yechenzuo@csis.u-tokyo.ac.jp (C.Y.); oguchi@csis.u-tokyo.ac.jp (T.O.); tang@ms.k.u-tokyo.ac.jp (Y.T.); | |
| 700 | 1 | |a Tang, Yuting |u Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-0882, Japan; yechenzuo@csis.u-tokyo.ac.jp (C.Y.); oguchi@csis.u-tokyo.ac.jp (T.O.); tang@ms.k.u-tokyo.ac.jp (Y.T.); | |
| 700 | 1 | |a Pei Xiangjun |u School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China | |
| 700 | 1 | |a Wu, Yufeng |u Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-0882, Japan; yechenzuo@csis.u-tokyo.ac.jp (C.Y.); oguchi@csis.u-tokyo.ac.jp (T.O.); tang@ms.k.u-tokyo.ac.jp (Y.T.); | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 13 (2025), p. 2280-2318 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3229156903/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3229156903/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3229156903/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |