Department of Geography, West University of Timisoara, Bd. V. Parvan 4, 300223, Timisoara, Romania.
Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan.
Sci Rep. 2022 Feb 8;12(1):2106. doi: 10.1038/s41598-022-06257-w.
This work aims at evaluating the sensitivity of landslide susceptibility mapping (LSM) to sampling design in lithologically-heterogeneous areas. We hypothesize that random sampling of the landslide absence data in such areas can be biased by statistical aggregation of the explanatory variables, which impact the model outputs. To test this hypothesis, we train a Random Forest (RF) model in two different domains, as follows: (1) in lithologically heterogeneous areas, and (2) in lithologically homogeneous domains of the respective areas. Two heterogeneous areas are selected in Japan (125 km) and Romania (497 km), based on existing landslide inventories that include 371 and 577 scarps, respectively. These areas are divided into two, respectively three domains, defined by lithological units that reflect relatively homogeneous topographies. Fourteen terrain attributes are derived from a 30 m SRTM digital elevation model and employed as explanatory variables. Results show that LSM is sensitive to a random sampling of the absence data in lithologically heterogeneous areas. Accuracy measures improve significantly when sampling and LSM are conducted in lithologically homogeneous domains, as compared to heterogeneous areas, reaching an increase of 9% in AUC and 17% in the Kappa index.
本研究旨在评估在岩性多变地区进行滑坡敏感性制图（LSM）时采样设计的敏感性。我们假设，在这些地区随机抽样无滑坡数据可能会受到解释变量统计聚集的影响，从而影响模型输出。为了验证这一假设，我们在两个不同的领域训练了随机森林（RF）模型，如下所示：（1）在岩性多变的地区，（2）在各自地区岩性均匀的区域。根据包含 371 个和 577 个滑坡陡坎的现有滑坡目录，在日本（125 公里）和罗马尼亚（497 公里）选择了两个岩性多变的地区。这些地区分别分为两个和三个区域，由反映相对均匀地形的岩性单元定义。从 30 m SRTM 数字高程模型中提取了 14 个地形属性，并作为解释变量。结果表明，在岩性多变地区，LSM 对无滑坡数据的随机抽样非常敏感。与岩性多变地区相比，在岩性均匀的区域进行采样和 LSM 时，精度指标显著提高，AUC 增加了 9%，Kappa 指数增加了 17%。
