From probabilistic back analyses to probabilistic run-out predictions of landslides: A case study of Heifangtai terrace, Gansu Province, China.

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]