E3S Web of Conferences 415, 05013 (2023)
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https://doi.org/10.1051/e3sconf/202341505013

Towards reliability-management for debris flow risk assessment
Julia Kowalski1*, Hu Zhao1 and Anil Yildiz1
1Methods for Model-based Development in Computational Engineering,

Faculty of Mechanical Engineering, RWTH Aachen, DE-52062 Aachen, GermanyAbstract. Recent progress in data-integrated simulation methods excelled our understanding of debris
flows including triggering mechanisms and dynamic run-out behavior. Research groups and geohazard
practitioners worldwide successfully integrate advanced simulations into workflows for hazard mapping.
However, many challenges remain in predictively applying such tools for accepted decision support. One
reason is our lack of a systematic approach to managing the simulations’ reliability. In this contribution, we
present results on an investigation to which extent the choice of data used for calibration influences thesimulation’s reliability. We start with introducing building blocks of a modular and extendible dataintegrated debris flow simulation toolchain developed by our group. Next, we introduce reliability as one
quality measure of a holistic debris flow simulation and discuss how it can be assessed. Based on a synthetic
example, we then show how different types of observed calibration data, such as impact area, deposit volume
or localized velocity measurements impacts on the subsequent forward simulation’s posterior probability
distribution, hence the simulation’s reliability. We conclude by discussing how linking a debris flow
simulation’s reliability to type, scope and resolution of the calibration data could offer a novel pathway
towards reliability management for debris flow risk assessment.

1 Introduction
Today, we are able to conduct advanced highperformance simulations of cascaded natural disasters
involving debris flow activity. High-performance
simulations of such geohazards open a revolutionary
opportunity for customized and dynamically adoptablehazard maps that support decision makers and allow for
the model-based design of effective mitigation measures
to save lives and prevent financial loss. Once acalibrated computational geohazard simulation tool
chain is available, its quality can be assessed indifferent ways: We can assess the plausibility of theunderlying process model given observations that areavailable to us. While this has often been done in an ad
hoc manner in the past, there is recently a push towards
more formalized Bayesian model selection techniques.
If there is strong reason to believe that the underlying
process model is highly appropriate, then we can assessthe data-integrated simulation model’s potential fortransfer: Can the calibrated model be used to predict ina different context, hence can it be transferred to an
event at a different location? A third option is to assess
the simulation model’s reliability: To which extent canwe trust the data-integrated simulation results, hence
how large is the model’s uncertainty?
This study addresses the third question, hence
reliability in mass flow simulations. Various techniques
have been proposed to assess uncertainty and reliability
including point estimate methods, first order second
moment and reliability methods, and Monte Carlosimulations, see [1] for an overview. While these studies

are designed to propagate parameter uncertainty througha mass flow simulation, they typically do not address
how reliability can be managed. Reliabilitymanagement is the central motivation for this work. In
section 2, we will introduce a modular mass flow
simulation tool chain used for this study. In section 3,we will present a case study based on which we discuss
how different types of observation data impact on thesimulation’s reliability. In section 4 we conclude andprovide an outlook how these findings could offer new
possibilities for reliability management in debris flowrisk assessment.

2 Simulation tool chain for reliability
assessment
Assessing reliability in debris flow simulations requires
combining probabilistic model parameter calibration
based on observations with a subsequent goal-oriented
forward uncertainty quantification [2]. The ability toeffectively manage reliability imposes further
requirements on modularity, computational-feasibility
and usability of such simulation tool chains. In ourresearch group, we develop a simulation tool chain
capable of both computationally feasible backwards
probabilistic parameter calibration and forward
uncertainty quantification in mass movements. It
follows a modular setup to facilitate a selective choiceof the central building blocks defining a modern mass
movement simulation tool chain (see figure 1).

* Corresponding author: kowalski@mbd.rwth-aachen.de© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 05013 (2023)
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approach itself will be generalized to other mass flow
scenarios in the future.

Fig. 1. The schematic shows the generic structure of a modulardebris flow simulation tool chain. Yellow indicates the processmodel, blue indicates necessary computational techniques andgreen indicates data integrated into the simulation tool chain.
Different aspects of the calibrated tool chain can be quality
assessed including its potential for being transferred to anothersite as well as its plausibility and reliability. Boxes boundedby red dashed lines indicate high-throughput tasks that arecomputationally expensive, and benefit from modern
surrogate modelling techniques.

The chosen physical process model (a) is at theheart along with its numerical solution scheme (b),e.g., an idealized mass point model, r.avaflow [3],or RAMMS [4]. The choice of the underlying
computationalmodel
definesthemodelparameters to-be-calibrated (c), such as Coulomb
friction, as well as the simulation’s output (d), e.g.,
the flow’s spatio-temporal height and velocity
distribution. The purpose of the simulation study
determines the relevant diagnostic variable ofinterest post-processed from the output (e), e.g.,
impact area, maximum run-out distance or factorof safety. Computational techniques are needed
not only for numerically solving the process model(b), but also to (potentially) introduce a surrogate
for speed-up and to facilitate high-throughput
tasks (f) – here based on Gaussian processemulation [6] - and to efficiently calibrate model
parameters (g) – here based on Bayesian Active
learning [5]. Finally, specific simulation andcalibration scenarios rely on static input data (h),such as topography and vegetation maps, and on
observables that comprise the training data set
used for calibration (i). The automated integrationof surrogate modeling techniques is particularly
powerful, as it facilitates previously unfeasible
computational tasks, such as global sensitivity
analysis and uncertainty quantification tasks [5,7].

Fig. 2. Posterior distribution as a result of Bayesian Active
learning of dry-Coulomb friction and turbulent friction
parameters given synthetic observations extracted from asimulated landslide close to Bondo, Switzerland. See [6] for
simulation scenario and approach to Bayesian parameter
estimation.

3 How observation data impacts onprediction reliability

Fig. 3. Posterior distribution as a result of joint Bayesian
Active learning of dry-Coulomb friction and turbulent friction
parameters given a combination of two types of synthetic
observations, namely deposit volume and maximum velocity
at location L1. See [6] for details about the simulation
scenario.

The goal of this study is to investigate the impact ofdifferent types of observed data on the reliability of asubsequent probabilistic forward simulation. For thesake of transparency and interpretability, we will revisit
previously published simulation results and interpretthem in light of a potential reliability management. The2

E3S Web of Conferences 415, 05013 (2023)
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In particular, we follow [6] in synthesizing
observations including maximum flow height andvelocity as well as deposition volume and impact areafrom a landslide simulation close to Bondo,
Switzerland. Posterior probability densities, as shown inFig. 2, are computationally inferred using grid
approximation based on a 100 × 100 grid over a
discretized parameter space following [6], namely [0.02,0.3] for dry-Coulomb and [100, 2200] m/s2 for turbulent
friction coefficients. A joint Bayesian parameter
estimation based on a smart combination of observables
yields a more concentrated posterior distribution incomparison to those based on one observable only as
demonstrated by [6], see Fig. 3.
So far, however, it is hard to judge how the estimatedposterior distribution impacts on the uncertainty of asubsequent forward simulation, hence the reliability ofthe subsequent prediction. To investigate this, theposterior distribution is first converted into probability
values associated with full grid discretization of the twodimensional parameter space. A Gaussian processemulator has then been trained to predict the impact
area, following [5]. The trained surrogate model has
been used to forward propagate previously identified
probabilities in parameter space, see Figs. 2 and 3.
Resulting values for the predicted impact area were
finally used to populate a histogram of 160 equally sizedbins between 2 to 6 x 106 m2. The value of each bincorrespond to the accumulated probabilities of its
generating simulation runs, see Fig. 4.
Highly reliable predictions are characterized by a very
narrow distribution. Predictions of low reliability arecharacterized by a broad probability distribution. Quite
interestingly, calibration based on integral values suchdeposit volume and impact area result in a predicted
impact area of low reliability, whereas a prediction
based on a simulation that was calibrated via localized
deposit heights is much more reliable. In particular, wefind that if a prediction of the mass flow’s impact area
is sought, it is a better strategy to calibrate based on, e.g.
a combination of point-based measurements than tocalibrate based on the impact area itself.

increasing the integrity and acceptance of model-based
policy making in geohazards engineering in the future.

4 Conclusions and outlook

1.

Fig. 4. Probability distributions of impact area predictions
based on model parameter posterior distributions of different
observables. Calibration based on deposit volume and impact
area imply a prediction of low reliability, whereas calibration
based on point-measurements, such as deposit area and(potentially) maximum flow velocities, or a combination,
therefore, yield higher reliability.

References

We showed that calibration of mass flow simulation
models based on different types of observable data may
significantly influence the reliability of a subsequentprediction. While the result is highly promising, it isvery important to emphasize the limiting aspects of thisstudy as it is based on synthetic data and assumes thatthe underlying Voellmy-Salm type shallow flow processmodel has proven to be highly plausible. Due to thecarefully designed synthetic nature of our study,
however, we have been able to demonstrate the large
potential of intelligent data selection for improving adata-integrated simulation tool chain’s reliability. In thefuture, type, scope and quality of the data can besystematically chosen to yield highly reliablepredictions. Optimizing data acquisition for reliability
management will be an important step towards

2.

3.

4.
5.
6.
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3

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