Emulator-based global sensitivity analysis for flow-like landslide run-out models

TL;DR

This paper introduces a Gaussian process emulation approach for global sensitivity analysis of flow-like landslide models, enabling efficient exploration of input interactions and uncertainties in complex simulations.

Contribution

The study integrates Gaussian process emulation into landslide modeling, allowing comprehensive sensitivity analysis and interaction detection with reduced computational costs.

Findings

First-order effects align with previous one-at-a-time analyses.

Strong interactions occur at flow path margins, diminishing with increased variability.

Interactions are more significant between friction coefficients than with release volume.

Abstract

Landslide run-out modeling involves various uncertainties originating from model input data. It is therefore desirable to assess the model's sensitivity. A global sensitivity analysis that is capable of exploring the entire input space and accounts for all interactions, often remains limited due to computational challenges resulting from a large number of necessary model runs. We address this research gap by integrating Gaussian process emulation into landslide run-out modeling and apply it to the open-source simulation tool r.avaflow. The feasibility and efficiency of our approach is illustrated based on the 2017 Bondo landslide event. The sensitivity of aggregated model outputs, such as the apparent friction angle, impact area, as well as spatially resolved maximum flow height and velocity, to the dry-Coulomb friction coefficient, turbulent friction coefficient and the release volume are studied. The results of first-order effects are consistent with previous results of common one-at-a-time sensitivity analyses. In addition to that, our approach allows to rigorously investigate interactions. Strong interactions are detected on the margins of the flow path where the expectation and variation of maximum flow height and velocity are small. The interactions generally become weak with increasing variation of maximum flow height and velocity. Besides, there are stronger interactions between the two friction coefficients than between the release volume and each friction coefficient. In the future, it is promising to extend the approach for other computationally expensive tasks like uncertainty quantification, model calibration, and smart early warning.