Department of Engineering, Wake Forest University, Winston-Salem, NC, USA.
Department of Geography & Sustainability, University of Tennessee, Knoxville, USA.
Sci Rep. 2023 Jan 31;13(1):1740. doi: 10.1038/s41598-023-28991-5.
The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of < 3°. The landslide susceptibility maps were then generated using the random forest model. The Receiver Operating Characteristic (ROC) curves and the Kappa index were used for accuracy assessment, while the Seed Cell Area Index (SCAI) was used for consistency assessment. The landslide susceptibility map produced using our proposed method has relatively high model fitting (0.87), prediction (0.85), and Kappa values (0.77). Even though the landslide susceptibility map produced by the slope-based sampling also has relatively high accuracy, the SCAI values suggest lower consistency. Furthermore, slope-based sampling is highly subjective; therefore, we recommend using MD -based absence data sampling for landslide susceptibility mapping.
滑坡敏感性图的准确性和质量取决于可用的滑坡位置和用于缺失数据（非滑坡位置）采样的策略。在本研究中，我们提出了一种基于马氏距离 (MD) 确定缺失数据采样临界值的客观方法。我们在孟加拉国兰加马蒂区的三个分区（乌帕齐拉）的滑坡敏感性制图中展示了这种方法，并将结果与基于坡度的缺失数据采样方法生成的滑坡敏感性图进行了比较。使用包括坡度、方位和平面曲率在内的 15 个滑坡成因因子，我们首先根据自由度为 14 的卡方分布确定了临界值 23.69。然后，使用该临界值确定了 261 个随机缺失数据的采样空间。相比之下，我们根据坡度阈值 < 3°选择了另一组缺失数据。然后使用随机森林模型生成滑坡敏感性图。使用接收者操作特征 (ROC) 曲线和 Kappa 指数进行准确性评估，而种子单元面积指数 (SCAI) 用于一致性评估。使用我们提出的方法生成的滑坡敏感性图具有相对较高的模型拟合度 (0.87)、预测度 (0.85) 和 Kappa 值 (0.77)。尽管基于坡度的采样生成的滑坡敏感性图也具有较高的准确性，但 SCAI 值表明一致性较低。此外，基于坡度的采样具有高度主观性；因此，我们建议使用基于 MD 的缺失数据采样进行滑坡敏感性制图。
