Vladivostok city and its surrounding areas have previously experienced rainfall-induced landslides, which have caused significant casualties and damage in high-density urban areas. As a result of anthropogenic factors, steep slopes in some areas reach 90°, which significantly affects the slope stability. The authors collected all available historical data about landslide incidents in the study area. A predictive model was derived using logistic regression and data on antecedent rainfall, cumulative precipitation, and daily rainfall intensity. The resulting model has relatively low precision and recall, which may reflect the lack of slope material parameters. Nonetheless, the balanced accuracy of 78% allows rainfall to be considered the most important causative factor of slope instability. The main advantage of the predictive model lies in its simplified mathematical expression and input rainfall data set based on measurements from one station with 24-h granularity. These results show promise for the further implementation of the model for the purpose of early warning.

符拉迪沃斯托克市及周边地区此前曾发生过降雨引发的山体滑坡，在高密度城市地区造成重大人员伤亡和破坏。受人为因素影响，部分地区陡坡达到90°，对边坡稳定性影响较大。作者收集了有关研究区滑坡事件的所有可用历史数据。使用逻辑回归和前期降雨量、累积降雨量和日降雨强度数据推导出预测模型。得到的模型精度和召回率相对较低，这可能反映了斜坡材料参数的缺乏。尽管如此，78% 的平衡精度使降雨量被认为是斜坡失稳的最重要原因。该预测模型的主要优势在于其简化的数学表达式和输入的降雨数据集，这些数据集基于一个 24 小时粒度的站点的测量结果。这些结果显示了进一步实施该模型以进行预警的前景。