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| Titel |
Influence of management of variables, sampling zones and land units on LR analysis for landslide spatial prevision |
| VerfasserIn |
R. Greco, M. Sorriso-Valvo |
| 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. 9 ; Nr. 13, no. 9 (2013-09-10), S.2209-2221 |
| Datensatznummer |
250085510
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| Publikation (Nr.) |
 copernicus.org/nhess-13-2209-2013.pdf |
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| Zusammenfassung |
Several authors, according to different methodological approaches, have
employed logistic Regression (LR), a multivariate statistical analysis
adopted to assess the spatial probability of landslide, even though its
fundamental principles have remained unaltered.
This study aims at assessing the influence of some of these methodological
approaches on the performance of LR, through a series of sensitivity
analyses developed over a test area of about 300 km2 in Calabria
(southern Italy).
In particular, four types of sampling (1 – the whole study area; 2 –
transects running parallel to the general slope direction of the study area
with a total surface of about 1/3 of the whole study area; 3 – buffers
surrounding the phenomena with a 1/1 ratio between the stable and the
unstable area; 4 – buffers surrounding the phenomena with a 1/2 ratio
between the stable and the unstable area), two variable coding modes (1 –
grouped variables; 2 – binary variables), and two types of elementary land
(1 – cells units; 2 – slope units) units have been tested. The obtained
results must be considered as statistically relevant in all cases (Aroc
values > 70%), thus confirming the soundness of the LR
analysis which maintains high predictive capacities notwithstanding the
features of input data.
As for the area under investigation, the best performing methodological
choices are the following: (i) transects produced the best results (0 < P(y) ≤ 93.4%; Aroc = 79.5%); (ii) as for sampling
modalities, binary variables (0 < P(y) ≤ 98.3%; Aroc = 80.7%)
provide better performance than ordinated variables; (iii) as for
the choice of elementary land units, slope units (0 < P(y) ≤
100%; Aroc = 84.2%) have obtained better results than cells matrix. |
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