Spatial probability assessment of landslide considering increases in pore-water pressure during rainfall and earthquakes: Case studies at Atsuma and Mt. Umyeon

•Considering the change in pore-water pressure due to rainfall and earthquake.•Good performance with real landslide events in Atsuma.•Cumulative rainfall strongly affect the area of susceptibility class.•Peak ground acceleration strongly affect the area of susceptibility class.•Excess pore-water pressure is the main reason for slope instability. Increased pore-water pressure due to rainfall infiltration and cyclic loading is a major cause of slope instability. Many studies have been carried out to assess rainfall-induced landslide spatial probability based on physical models, combining hydrological models to analyze changes in pore-water pressure on slopes due to rainfall. However, the generation of pore-water pressure due to seismic loading is often disregarded during assessments of earthquake-induced landslide susceptibility. Hence, in this paper, we propose a model to assess landslide spatial probability that takes into account increased pore-water pressure during both rainfall and earthquakes. The procedure for the proposed method includes two main steps. In step 1, we analyze the change in the groundwater table due to rainfall infiltration and subsurface flow during rainfall. In step 2, the slope safety factor is calculated using an infinite slope model, considering the generation of excess pore-water pressure under cyclic loading during earthquakes. Landslide spatial probability is established based on the slope factor of safety. We validated the proposed model by analyzing rainfall-earthquake-induced landslide events occurring on September 6, 2018 in Atsuma town, Japan. According to our results, the area under the receiver operating characteristic curve of the Atsuma landslide data is 82.4% and the true-positive rate of unstable slope classification is 98.1%. The proposed model was then applied to Mt. Umyeon, Korea, to assess the spatial probability of rainfall-earthquake-induced landslide. Our model classifies the likelihood of landslide occurrence according to four susceptibility levels: high, moderate, low and very low. We also compared our results to those of previous models and show that the proposed approach may provide reasonably accurate predictions of landslide spatial probability during rainfall and earthquake events.