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TY - THES AU - Zhao, Hu TI - Gaussian processes for sensitivity analysis, Bayesian inference, and uncertainty quantification in landslide research PB - Rheinisch-Westfälische Technische Hochschule Aachen VL - Dissertation CY - Aachen M1 - RWTH-2021-11693 SP - 1 Online-Ressource : Illustrationen PY - 2021 N1 - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2022 N1 - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021 AB - Landslides are common natural hazards occurring around the world. They pose an ongoing threat to lives, properties, and environment. Driven by the practical need to predict hazards of future landslides and design mitigation strategies, various physics-based landslide run-out models have been developed in the past decades. To achieve reliable and transparent simulation-based risk assessment and mitigation design, comprehensive understanding of the various uncertainties associated with these models is required. However, advanced statistical methods that are capable of properly addressing the uncertainties are often not applicable due to the computational bottleneck resulting from the relatively long run time of a single simulation and the large number of necessary simulations. To address the research gap, new methodologies are developed and studied in this thesis. They make up a unified framework that allows us to systematically, routinely, and efficiently investigate both forward and inverse problems resulting from the various uncertainties. Chapter 1 introduces the background, frames the research gap, and motivates this study. Chapter 2 and 3 present theories of the two essential components of the unified framework, namely physics-based landslide run-out models and data-driven Gaussian process emulators. Chapter 4 presents a new methodology for efficient variance-based global sensitivity analyses of landslide run-out models. The methodology couples depth-averaged landslide run-out models, variance-based sensitivity analyses, robust multivariate Gaussian process emulation techniques, and an algorithm accounting for the emulator-uncertainty. Its feasibility and efficiency are validated by a case study based on the 2017 Bondo landslide event. The results show that it can recover common findings in the literature and provides further information on interactions between input variables along the full flow path. Chapter 5 presents a new methodology for efficient parameter calibration of landslide run-out models. It is developed by integrating depth-averaged landslide run-out models, Bayesian inference, Gaussian process emulation, and active learning. A case study using the new method is conducted based on the 2017 Bondo landslide event with synthetic observed data. The results show that the method is capable of correctly calibrating the rheological parameters and greatly improving the computational efficiency. Chapter 6 is devoted to uncertainty quantification of landslide run-out models. The focus is put on topographic uncertainty which is mostly overlooked in current practice. Two types of geostatistical methods are used to study the impact of topographic uncertainty on landslide run-out modeling based on the 2008 Yu Tung landslide event. It is found that topographic uncertainty significantly affects landslide run-out modeling, depending on how well the underlying flow path is represented. In addition, the close relation between the two geostatistical methods and Gaussian processes is revealed. Based on it, a new method that employs Karhunen–Loeve expansion to reduce the dimensionality of topographic uncertainty is proposed. It has great potentials to make Gaussian process emulation also applicable for high-dimensional topographic uncertainty and therefore allows us to treat topographic uncertainty within the unified framework. Chapter 7 provides concluding remarks and recommendations for future work. LB - PUB:(DE-HGF)11 DO - DOI:10.18154/RWTH-2021-11693 UR - https://publications.rwth-aachen.de/record/836921 ER -