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Loess landslides pose significant physical and environmental threats to communities of the Heifangtai Terrace in Gansu Province, China. Estimating potentially affected areas using dynamic numerical simulations provides a foundation for risk assessment and management. However, current studies often use rheological input parameters derived from the back analysis of an individual historical landslide, which requires that the potential landslide is quite similar to the historical landslide to guarantee the accuracy of predictions. We collected data from 20 loess flowslides that occurred from 2015 to 2019 in the Heifangtai Terrace. A friction model embedded in MassFlow was used to simulate run-out behavior. Accumulation depths at multiple control points were used as observation information. A multi-objective optimization back analysis was used to calibrate optimal rheological parameters for each flowslide, yielding a region-specific database of 20 datasets for statistical analyses. Finally, two flowslides that occurred in 2019 were employed as potential landslides to conduct probabilistic run-out predictions based on the database. These predictions agreed well with the observed accumulation areas in both cases, thus validating our method. This database will be useful for future landslide run-out predictions in this area. [ABSTRACT FROM AUTHOR]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

The input parameters of dynamic numerical models for landslide run-out distance simulations cannot be measured accurately, which results in significant uncertainties in the predictions obtained using these models. To address this issue, a novel framework combining probabilistic back analyses and probabilistic predictions of landslide run-out distance is developed in this study, with the aim of ensuring the reliability of run-out predictions in risk assessments. First, a probabilistic back analysis method is applied to calibrate the input parameters and quantify their uncertainties; the calibrated parameters are then used for a probabilistic forward run-out prediction. Unlike the trial and error calibration approach, the back analysis method is implemented probabilistically by modeling the input parameters as random variables and efficiently improving their distributions through Markov chain Monte Carlo-based Bayesian inference using the observed run-out distance of a previous landslide event. These improved posterior distributions are employed to obtain the run-out distance exceedance probability curve of a potential landslide similar to the previous event; this curve can be used to estimate the probability of the run-out distance being exceeded at different locations along the landslide's path. By selecting different threshold values for the exceedance probabilities, a landslide hazard zoning map can be generated, serving as a valuable tool for landslide risk assessment and management. Moreover, a Kriging-based surrogate model is used to approximate the dynamic numerical model employed in this study, which significantly improves the computational efficiency of the proposed methodology. Finally, two sequential landslides that have occurred in the Heifangtai terrace of Gansu Province, China, are considered as a case study to demonstrate the performance of the proposed methodology. • Propose a probabilistic framework for reliable landslide run-out distance modeling. • Probabilistically calibrate the numerical model using observed run-out distance. • Assess landslide run-out distance exceedance probability with the calibrated results. • Kriging-based surrogate model significantly improves computational efficiency. • Results can be employed in quantitative risk assessment of individual landslides. [ABSTRACT FROM AUTHOR]