The predictive hazard analysis at a detailed scale for debris flow runout analysis can be improved significantly through reliable estimation of the input parameters. In this study, a method for database establishment of input parameters at a site-specific scale was laid out for the predictive-based debris flow hazard assessment under extreme rainfall. The adoption of the DAN-3D code necessitated the estimation of three main input parameters: initial volume, bulk basal frictional angle, and growth rate. The initial volume was assessed using a 3D coupled finite element seepage and limit equilibrium-based slope stability analysis. An artificial neural network-based model was developed using 27 debris flow events for predicting the basal bulk frictional angle and consisted of eight factors: plan curvature, profile curvature, percentage of fine content, D (50), initial unit weight, initial volume, relative relief ratio, and channel length. Finally, the growth rate was estimated using the previously assessed initial volume, soil depth, and the approximate runout length. The proposed method was validated by application to the Raemian slope in the Woomyeon mountain region, Seoul, for the extreme rainfall event of 27 July 2011. The analysis yielded a final volume of 53,067.9 m(3), a velocity upon arrival on the road of 26.81 m/s, and an approximately 0.5-m debris thickness concentrated near the Raemian apartments. The comparison of the predicted debris flow path and debris flow velocity with the actual event demonstrates good similarity and provides a conservative estimate of the volume. This study therefore illustrates the importance of an input parameter database in providing a reliable debris flow runout hazard assessment.