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Development of an urban landslide cellular automata model: a case study of North Vancouver, Canada

机译:城市滑坡元胞自动机模型的开发:以加拿大北温哥华为例

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Many GIS-based landslide models require detailed datasets that are ideally collected from field measurements, which can incur high costs for carrying out surveys. Even when the data is on hand, implementing physics-based slope stability techniques can be difficult. Common research practice uses differential equations to characterize the dynamic flow of a landslide, but it is often laborious without making substantial simplifications. A possible solution is to implement a cellular automata modeling approach, which represents both spatial and temporal components, to simulate the dynamics of the landslide propagation process. In this study, a simplified cellular automata model is developed for the effective prediction of landslide runouts, where the data requirement is a high resolution digital elevation model (DEM). Parameters, such as slope and slope curvature features, are derived from the DEM and coupled with logistic regression. The developed model is implemented on the Patrick and Dawson-Chu Slide in North Vancouver, Canada. The results from this study site were favorable, given almost 90% agreement between simulated landslides and data obtained for real landslides. In addition, sensitivity analysis was performed on the initiation sites to test the model logic and outputs of the landslide flow.
机译:许多基于GIS的滑坡模型都需要详细的数据集,这些数据集理想地是从野外测量中收集的,这可能会导致进行调查的高昂成本。即使有数据,也很难实施基于物理学的边坡稳定性技术。普通的研究实践使用微分方程来描述滑坡的动态流动,但是在没有进行实质性简化的情况下通常很费力。一种可能的解决方案是实现一种代表空间和时间分量的元胞自动机建模方法,以模拟滑坡传播过程的动力学。在这项研究中,开发了一种简化的元胞自动机模型,用于有效预测滑坡跳动,其中数据需求是高分辨率数字高程模型(DEM)。参数(例如坡度和坡度曲率特征)是从DEM导出的,并与逻辑回归结合。开发的模型在加拿大北温哥华的Patrick和Dawson-Chu Slide上实现。鉴于模拟滑坡与实际滑坡获得的数据之间几乎有90%的一致性,因此该研究地点的结果令人满意。另外,在初始位置进行了敏感性分析,以测试滑坡流的模型逻辑和输出。

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