Compiling an inventory is a fundamental step for carrying out assessments of landslide hazards. However, data in sufficient quantity and quality are not always available. Thus, this study puts forward an approach for drawing up a landslide inventory using textual data from telephone records, and for mapping hazards of landslides in an urban area. Forty thousand seven hundred ninety-two textual records and the naive Bayes algorithm were used to classify them, and these form the landslide inventory. After creating the inventory, the random forest algorithm with 12 conditioning variables was used to map landslide hazards. The text classification model obtained an accuracy of 0.8671 and a Kappa index of 0.8038. The hazard mapping model obtained accuracy of 0.9503 and an AUC (area under the curve)-ROC (receiver operating characteristics) of 0.9870. The results produced by the model were also compared with real landslides reported in news reports and were shown to be close to what had happened, thus demonstrating the ability of the proposed approach to predict landslides. Finally, the proposed approach can be used in simulation environments, thereby supporting strategic decision-making associated with hazard analysis.

编制清单是进行滑坡灾害评估的基本步骤。但是，并非总是有足够数量和质量的数据。因此，本研究提出了一种利用电话记录中的文本数据来编制滑坡清单的方法，以及用于绘制市区内滑坡危害的地图。使用4792个文本记录和朴素的贝叶斯算法对它们进行分类，这些记录形成了滑坡清单。创建清单后，使用具有12个条件变量的随机森林算法来绘制滑坡灾害图。文本分类模型的准确度为0.8671，Kappa指数为0.8038。危害映射模型获得的精度为0.9503，AUC（曲线下面积）-ROC（接收器工作特性）为0.9870。该模型产生的结果也与新闻报道中报道的真实滑坡进行了比较，并显示出与发生的事件接近，从而证明了该方法预测滑坡的能力。最后，所提出的方法可以在模拟环境中使用，从而支持与危害分析相关的战略决策。