Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression—one of the most widely used susceptibility models—to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.

中文翻译：

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滑坡敏感性估计有多可靠？

许多当代滑坡研究都与预测和绘制边坡失稳的敏感性有关。许多研究依赖于具有环境预测器的广义线性模型，这些模型是用从绘制的滑坡边缘内外收集的数据进行训练的。这些模型的性能是否以及如何取决于样本大小、位置或时间在很大程度上仍未得到测试。我们通过探索多元逻辑回归（最广泛使用的敏感性模型之一）对覆盖东部部分地区的两个独立清单（即历史和多时间）中滑坡不同部分采样数据的敏感性来解决这个问题。吉尔吉斯斯坦费尔干纳盆地边缘。我们发现，仅考虑滑坡下部的区域，因此很可能是它们的沉积物，可以将模型性能比使用整个滑坡区域的参考案例提高 10% 以上，尤其是对于中等大小的滑坡。因此，使用滑坡脚趾区域可能足以满足此特定模型的需求，并且在中亚这一地区滑坡疤痕模糊或隐藏的情况下很有用。随着时间的推移逐渐更新和添加更多滑坡数据后，模型性能略有变化。我们得出的结论是，研究区域的滑坡敏感性估计在大约十年内对数据变化基本上不敏感。空间或时间分层抽样对模型性能的影响很小。我们的发现要求对动态敏感性的概念及其在数据驱动模型中的解释进行更广泛的测试，