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| Titel |
Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results |
| VerfasserIn |
V. Capecchi, M. Perna, A. Crisci |
| 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 Sciences ; 15, no. 1 ; Nr. 15, no. 1 (2015-01-13), S.75-95 |
| Datensatznummer |
250119273
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| Publikation (Nr.) |
 copernicus.org/nhess-15-75-2015.pdf |
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| Zusammenfassung |
Our study is aimed at estimating the added value provided by Numerical
Weather Prediction (NWP) data for the modelling and prediction of
rainfall-induced shallow landslides. We implemented a quantitative indirect
statistical modelling of such phenomena by using, as input predictors, both
geomorphological, geological, climatological information and numerical data
obtained by running a limited-area weather model. Two standard statistical
techniques are used to combine the predictor variables: a generalized
linear model and Breiman's random forests. We tested these models for two rainfall
events that occurred in 2011 and 2013 in Tuscany region (central Italy).
Modelling results are compared with field data and the forecasting skill is
evaluated by mean of sensitivity–specificity receiver operating
characteristic (ROC) analysis. In the 2011 rainfall event, the random forests
technique performs slightly better than generalized linear model with area
under the ROC curve (AUC) values around 0.91 vs. 0.84. In the 2013 rainfall
event, both models provide AUC values around 0.7.
Using the variable importance output provided by the random forests
algorithm, we assess the added value carried by numerical weather forecast.
The main results are as follows: (i) for the rainfall event that occurred in 2011 most of the
NWP data, and in particular hourly rainfall intensities, are classified as "important"
and (ii) for the rainfall event that occurred in 2013 only NWP soil moisture data in the first
centimetres below ground is found to be relevant for landslide assessment.
In the discussions we argue how these results are connected to the type of
precipitation observed in the two events. |
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