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Ensuring the safety of personnel and providing practical escape routes for potential victims play essential roles in reducing the human vulnerability to site-specific landslides. However, the escape guidelines adopted by government departments and public welfare organizations are vague and unfocused because they ignore the features of individual people and spatial differences among hazard zones. In this paper, a scenario simulation model for quantitatively evaluating the escape behavior and human vulnerability was proposed using a Python platform based on the landslide runout process and hazard zone characteristics. A database that includes detailed information for the hazard zone's demographic structure and behavioral characteristics was established based on sampling surveys and field investigations. The probability values of scenario input parameters such as the escape route and speed were calculated and quantified by classic probability theory. The registered population over 65 years old accounted for 13.46% of the total, and most residents had no more than a primary school education background. Older adults were inclined to escape a moving landslide by running parallel to the sliding direction, although the public considers this direction to be the most dangerous. The exposure time in the hazard zone is 930 to 980 h per hectare during a corn planting year; the model simulation revealed that the cumulative mortality can be significantly reduced by promoting disaster prevention awareness and improving the advance warning time. This research and the developed quantitative human vulnerability framework provide user-friendly disaster prevention rehearsal guidelines and a reference for human disaster mitigation. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]