An artificial intelligence-based approach to predicting seismic hillslope stability under extreme rainfall events in the vicinity of Wolsong nuclear power plant, South Korea

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.