Bhuyan Kushanav, Rana Kamal, Ferrer Joaquin V, Cotton Fabrice, Ozturk Ugur, Catani Filippo, Malik Nishant
Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, Padova, 35129, Veneto, Italy.
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, 14473, Brandenburg, Germany.
Nat Commun. 2024 Mar 25;15(1):2633. doi: 10.1038/s41467-024-46741-7.
The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models' performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flows, and falls, by analyzing landslides' 3D shapes. By examining landslide topological properties, we discover distinct patterns in their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy by applying topological properties in identifying landslide movements across diverse geographical and climatic regions, including Italy, the US Pacific Northwest, Denmark, Turkey, and Wenchuan in China. Furthermore, we demonstrate a real-world application on undocumented datasets from Wenchuan. Our work introduces a paradigm for studying landslide shapes to understand their underlying movements through the lens of landslide topology, which could aid landslide predictive models and risk evaluations.
尽管预测模型有所进步，但山体滑坡造成的死亡人数和经济损失仍在不断上升。这些模型的性能有限，因为在开发过程中使用的山体滑坡数据库往往缺少关键信息，例如潜在的运动类型。本研究介绍了一种通过分析山体滑坡的三维形状来识别山体滑坡运动（如滑动、流动和坠落）的方法。通过研究山体滑坡的拓扑特性，我们发现了它们形态上的不同模式，这表明了不同的运动，包括具有多个耦合运动的复杂运动。通过应用拓扑特性来识别包括意大利、美国太平洋西北部、丹麦、土耳其和中国汶川在内的不同地理和气候区域的山体滑坡运动，我们实现了80%-94%的准确率。此外，我们展示了对来自汶川的未记录数据集的实际应用。我们的工作引入了一种通过山体滑坡拓扑学来研究山体滑坡形状以了解其潜在运动的范式，这有助于山体滑坡预测模型和风险评估。
