A spectral clustering search algorithm for predicting shallow landslide size and location
A spectral clustering search algorithm for predicting shallow landslide size and location
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DOI：
10.1002/2014jf003137
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发表时间：
2015-02
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通讯作者：
D. Bellugi;D. Milledge;W. Dietrich;J. McKean;J. Perron;Erik B. Sudderth;Brian Kazian
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文献类型：
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作者：
D. Bellugi;D. Milledge;W. Dietrich;J. McKean;J. Perron;Erik B. Sudderth;Brian Kazian
浅层滑坡的潜在危险性和地貌意义取决于其位置和规模。常用的一维稳定性模型不包括侧向阻力，无法预测滑坡的大小。多维模型必须应用于特定的几何形状，这是不知道的先验，并测试所有可能的几何形状是计算禁止。我们提出了一个有效的确定性搜索算法的基础上，谱图理论和耦合的多维稳定性模型预测离散的滑坡在应用程序中的规模比一个单一的山坡使用网格化的空间数据。该算法是通用的，仅假设当作用在单元簇上的驱动力超过其边缘上的阻力时会导致不稳定，并且单元簇表现为在土壤-基岩界面处具有破坏面的刚性块体。该算法在合成景观上恢复不同形状和大小的不稳定单元的预定义集群，使用现场测量的物理参数预测观察到的浅层滑坡的大小，位置和形状，并且对输入参数的适度变化具有鲁棒性。搜索算法确定补丁的潜在的不稳定大面积的稳定景观。在这些斑块内，将有许多不同的安全系数小于1的细胞组合，这表明局部条件的细微变化（例如，孔隙压力和根部强度）可以确定特定位置处的最终形式和确切位置。尽管如此，这里提出的测试表明，搜索算法能够预测浅层滑坡的大小以及跨景观的位置。
The potential hazard and geomorphic significance of shallow landslides depend on their location and size. Commonly applied one‐dimensional stability models do not include lateral resistances and cannot predict landslide size. Multidimensional models must be applied to specific geometries, which are not known a priori, and testing all possible geometries is computationally prohibitive. We present an efficient deterministic search algorithm based on spectral graph theory and couple it with a multidimensional stability model to predict discrete landslides in applications at scales broader than a single hillslope using gridded spatial data. The algorithm is general, assuming only that instability results when driving forces acting on a cluster of cells exceed the resisting forces on its margins and that clusters behave as rigid blocks with a failure plane at the soil‐bedrock interface. This algorithm recovers predefined clusters of unstable cells of varying shape and size on a synthetic landscape, predicts the size, location, and shape of an observed shallow landslide using field‐measured physical parameters, and is robust to modest changes in input parameters. The search algorithm identifies patches of potential instability within large areas of stable landscape. Within these patches will be many different combinations of cells with a Factor of Safety less than one, suggesting that subtle variations in local conditions (e.g., pore pressure and root strength) may determine the ultimate form and exact location at a specific site. Nonetheless, the tests presented here suggest that the search algorithm enables the prediction of shallow landslide size as well as location across landscapes.