Rainfall and earthquakes are two significant triggering factors of mass movement. Since the Gyeongju earthquake on 12 September 2016, which took place near the Wolsong nuclear power plant, many concerns have been raised about the threat posed by landslides during intense rainfall. In this study, we developed a new methodological approach to assess the stability of hillslopes at the catchment scale. We applied a geographical information system (GIS)-based pseudo-static model to 10,000 representative sample points by coupling the steady state infiltration corresponding to extreme rainfall and seismic force. Thus, we obtained the factor of safety of the representative sample points and set it as our target variable. The target variable was divided into two subsets: 80% of the data was used to train the model and 20% was reserved for testing purposes. We then applied a deep learning neural network method to incorporate other spatial geo-environmental data such as topographic, hydrologic, soil, forest, and geology, i.e., independent variables that can be used to predict the factor of safety in the catchment scale. The accuracy of the model was assessed using Pearson’s correlation coefficient, which was 0.97 and 0.98 and root mean square error 0.301 and 0.290 in the cases of the training and testing data, respectively. The prediction results indicate that the integration approach produces reliable, accurate landslide susceptibility maps, which may be helpful to researchers working on landslide management strategies.

降雨和地震是群众运动的两个重要触发因素。自2016年9月12日庆州地震发生在卧城核电站附近以来，人们就强烈降雨引发的山体滑坡威胁提出了许多关切。在这项研究中，我们开发了一种新的方法学方法来评估集水区规模的山坡的稳定性。通过耦合对应于极端降雨和地震力的稳态渗透，我们将基于地理信息系统（GIS）的伪静态模型应用于10,000个代表性样本点。因此，我们获得了代表性样本点的安全系数，并将其设置为我们的目标变量。目标变量分为两个子集：80％的数据用于训练模型，而20％的数据用于测试。然后，我们应用了深度学习神经网络方法来合并其他空间地理环境数据，例如地形，水文，土壤，森林和地质，即可以用来预测流域规模安全因素的自变量。使用Pearson相关系数评估模型的准确性，在训练和测试数据的情况下，相关系数分别为0.97和0.98，均方根误差为0.301和0.290。预测结果表明，这种整合方法可以生成可靠，准确的滑坡敏感性图，这可能对研究滑坡管理策略的研究人员有所帮助。可用于预测流域规模安全因素的自变量。使用Pearson相关系数评估模型的准确性，在训练和测试数据的情况下，相关系数分别为0.97和0.98，均方根误差为0.301和0.290。预测结果表明，这种整合方法可以生成可靠，准确的滑坡敏感性图，这可能对研究滑坡管理策略的研究人员有所帮助。可用于预测流域规模安全因素的自变量。使用Pearson相关系数评估模型的准确性，在训练和测试数据的情况下，相关系数分别为0.97和0.98，均方根误差为0.301和0.290。预测结果表明，这种整合方法可以生成可靠，准确的滑坡敏感性图，这可能对研究滑坡管理策略的研究人员有所帮助。