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| Titel |
Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues |
| VerfasserIn |
F. Catani, D. Lagomarsino, S. Segoni, V. Tofani |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 11 ; Nr. 13, no. 11 (2013-11-13), S.2815-2831 |
| Datensatznummer |
250085553
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| Publikation (Nr.) |
 copernicus.org/nhess-13-2815-2013.pdf |
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| Zusammenfassung |
Despite the large number of recent advances and developments in landslide
susceptibility mapping (LSM) there is still a lack of studies focusing on
specific aspects of LSM model sensitivity. For example, the influence of
factors such as the survey scale of the landslide conditioning variables
(LCVs), the resolution of the mapping unit (MUR) and the optimal number and
ranking of LCVs have never been investigated analytically, especially on
large data sets.
In this paper we attempt this experimentation concentrating on the impact of
model tuning choice on the final result, rather than on the comparison of
methodologies. To this end, we adopt a simple implementation of the random
forest (RF), a machine learning technique, to produce an ensemble of
landslide susceptibility maps for a set of different model settings, input
data types and scales. Random forest is a combination of Bayesian trees that
relates a set of predictors to the actual landslide occurrence. Being it a
nonparametric model, it is possible to incorporate a range of numerical or
categorical data layers and there is no need to select unimodal training
data as for example in linear discriminant analysis. Many widely
acknowledged landslide predisposing factors are taken into account as mainly
related to the lithology, the land use, the geomorphology, the structural
and anthropogenic constraints. In addition, for each factor we also include
in the predictors set a measure of the standard deviation (for numerical
variables) or the variety (for categorical ones) over the map unit.
As in other systems, the use of RF enables one to estimate the relative
importance of the single input parameters and to select the optimal
configuration of the classification model. The model is initially applied
using the complete set of input variables, then an iterative process is
implemented and progressively smaller subsets of the parameter space are
considered. The impact of scale and accuracy of input variables, as well as
the effect of the random component of the RF model on the susceptibility
results, are also examined. The model is tested in the Arno River basin
(central Italy). We find that the dimension of parameter space, the mapping
unit (scale) and the training process strongly influence the classification
accuracy and the prediction process.
This, in turn, implies that a careful sensitivity analysis making use of
traditional and new tools should always be performed before producing final
susceptibility maps at all levels and scales. |
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