In Japan, the frequency of sudden heavy rain events has recently increased, causing slope failures that in turn increase rates of damage to transit infrastructure such as railways and roads. To reduce this damage, there is a need to identify locations near railroad tracks that are at risk of slope failure. Thus, an assessment that predicts whether or not damage will occur due to external forces such as heavy rains is required, rather than a simple relative risk assessment based on identifying locations similar to those damaged in previous events. In this study, we developed a method for time series stability assessment of slopes during heavy rains using digital topographic data. This method uses topographic data to estimate topsoil thickness, which contributes to stability, and soil strength, which is affected by the root systems of vegetation on slopes. Using differences in these parameters between tree species and forest type, we were able to calculate slope stability and simulate slope failure during rainfall. The simulations allowed us to evaluate locations along routes where previous failures occurred, and to identify at-risk locations that have not yet experienced slope failure. This approach will improve forest management based on risk assessments for intensifying heavy rains.