Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models

Abstract

This study presents a novel Bayesian framework for statistical calibration of spatially distributed physical-based landslide prediction models. The calibration process is formulated in a statistical setting with the model parameters simulated as spatially variable with random fields and the model calibration defined within the Bayesian framework. The implementation of such calibration process is challenging due to large numbers of calibration parameters and high-dimensional likelihood functions, which are central in establishing a relation between observations and the corresponding model predictions. The former challenge was resolved by reformulating the Bayesian updating problem as an equivalent reliability problem and solving it with efficient reliability methods. The latter challenge was resolved by developing novel lower-dimensional approximate likelihood formulations, suitable for the interpretation of landslide initiation zones, based on the Approximate Bayesian Computation method. The novelties of the proposed approach stem from describing landslide model parameters as spatially variable, development of a statistical framework to calibrate landslide prediction models, and introduction of approximate likelihood formulations.