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| Titel |
GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model |
| VerfasserIn |
O. G. Terranova, S. L. Gariano, P. Iaquinta, G. G. R. Iovine |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 7 ; Nr. 8, no. 7 (2015-07-07), S.1955-1978 |
| Datensatznummer |
250116448
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| Publikation (Nr.) |
 copernicus.org/gmd-8-1955-2015.pdf |
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| Zusammenfassung |
GASAKe is a new hydrological model aimed at forecasting
the triggering of landslides. The model is based on genetic algorithms and
allows one to obtain thresholds for the prediction of slope failures using
dates of landslide activations and rainfall series. It can be applied to
either single landslides or a set of similar slope movements in a homogeneous
environment.
Calibration of the model provides families of optimal, discretized solutions
(kernels) that maximize the fitness function. Starting from the kernels, the
corresponding mobility functions (i.e., the predictive tools) can be
obtained through convolution with the rain series. The base time of the
kernel is related to the magnitude of the considered slope movement, as well
as to the hydro-geological complexity of the site. Generally, shorter base
times are expected for shallow slope instabilities compared to larger-scale
phenomena. Once validated, the model can be applied to estimate the timing
of future landslide activations in the same study area, by employing
measured or forecasted rainfall series.
Examples of application of GASAKe to a medium-size slope
movement (the Uncino landslide at San Fili, in Calabria, southern Italy) and
to a set of shallow landslides (in the Sorrento Peninsula, Campania, southern
Italy) are discussed. In both cases, a successful calibration of the model
has been achieved, despite unavoidable uncertainties concerning the dates of
occurrence of the slope movements. In particular, for the Sorrento Peninsula
case, a fitness of 0.81 has been obtained by calibrating the model against 10
dates of landslide activation; in the Uncino case, a fitness of 1 (i.e.,
neither missing nor false alarms) has been achieved using five activations.
As for temporal validation, the experiments performed by considering further
dates of activation have also proved satisfactory.
In view of early-warning applications for civil protection, the capability
of the model to simulate the occurrences of the Uncino landslide has been
tested by means of a progressive, self-adaptive procedure. Finally, a
sensitivity analysis has been performed by taking into account the main
parameters of the model.
The obtained results are quite promising, given the high performance of the
model against different types of slope instabilities characterized by
several historical activations. Nevertheless, further refinements are still
needed for application to landslide risk mitigation within early-warning and
decision-support systems. |
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