The propagation of inventory-based positional errors into statistical landslide susceptibility models
There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-12-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/16/2729/2016/nhess-16-2729-2016.pdf |
| Summary: | There is unanimous agreement that a precise spatial representation of past
landslide occurrences is a prerequisite to produce high quality statistical
landslide susceptibility models. Even though perfectly accurate landslide
inventories rarely exist, investigations of how landslide inventory-based
errors propagate into subsequent statistical landslide susceptibility models
are scarce.
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The main objective of this research was to systematically examine whether
and how inventory-based positional inaccuracies of different magnitudes
influence modelled relationships, validation results, variable importance
and the visual appearance of landslide susceptibility maps. The study was
conducted for a landslide-prone site located in the districts of Amstetten
and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory
was available.
<br><br>
The methodological approach comprised an artificial introduction of
inventory-based positional errors into the present landslide data set and an
in-depth evaluation of subsequent modelling results. Positional errors were
introduced by artificially changing the original landslide position by a
mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise
response variables were separately used to train logistic regression models.
Odds ratios of predictor variables provided insights into modelled
relationships. Cross-validation and spatial cross-validation enabled an
assessment of predictive performances and permutation-based variable
importance. All analyses were additionally carried out with synthetically
generated data sets to further verify the findings under rather controlled
conditions.
<br><br>
The results revealed that an increasing positional inventory-based error was
generally related to increasing distortions of modelling and validation
results. However, the findings also highlighted that interdependencies
between inventory-based spatial inaccuracies and statistical landslide
susceptibility models are complex. The systematic comparisons of 12 models
provided valuable evidence that the respective error-propagation was not
only determined by the degree of positional inaccuracy inherent in the
landslide data, but also by the spatial representation of landslides and the
environment, landslide magnitude, the characteristics of the study area, the
selected classification method and an interplay of predictors within
multiple variable models. Based on the results, we deduced that a direct
propagation of minor to moderate inventory-based positional errors into
modelling results can be partly counteracted by adapting the modelling
design (e.g. generalization of input data, opting for strongly generalizing
classifiers). Since positional errors within landslide inventories are
common and subsequent modelling and validation results are likely to be
distorted, the potential existence of inventory-based positional
inaccuracies should always be considered when assessing landslide
susceptibility by means of empirical models. |
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| ISSN: | 1561-8633 1684-9981 |