The distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslide susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquake cases, which means information about the distance to the surface rupture is lacking. In this study, a new influencing factor named coseismic ground deformation was added to remedy this shortcoming. The Mid-Niigata prefecture earthquake was regarded as the study case. To select a more suitable model for generating the landslide susceptibility map, three commonly used models named logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) were also conducted to assess landslide susceptibility. The performances of these three models were evaluated with the receiver operating characteristic curve. The calculated results showed that the ANN model has the highest area under the curve (AUC) value of 0.82. As the earthquake triggered more landslides in the epicenter area, which makes it more prone to landslides in further earthquakes, the susceptibility analysis at two different mapping scales (the whole study area and the epicenter area) was also applied.