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@PHDTHESIS{Zhao:836921,
author = {Zhao, Hu},
othercontributors = {Reicherter, Klaus and Kowalski, Julia},
title = {{G}aussian processes for sensitivity analysis, {B}ayesian
inference, and uncertainty quantification in landslide
research},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2021-11693},
pages = {1 Online-Ressource : Illustrationen},
year = {2021},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2022; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2021},
abstract = {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.},
cin = {080003 / 531320 / 530000},
ddc = {550},
cid = {$I:(DE-82)080003_20140620$ / $I:(DE-82)531320_20140620$ /
$I:(DE-82)530000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2021-11693},
url = {https://publications.rwth-aachen.de/record/836921},
}
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