Articles | Volume 8, issue 7
Methods for assessment of models
07 Jul 2015
Methods for assessment of models |  | 07 Jul 2015

GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model

O. G. Terranova, S. L. Gariano, P. Iaquinta, and G. G. R. Iovine

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Cited articles

Aleotti, P.: A warning system for rainfall-induced shallow failures, Eng. Geol., 73, 247–265, 2004.
Alfieri, L., Salamon, P., Pappenberger, F., Wetterhall, F., and Thielen, J.: Operational early warning systems for water-related hazards in Europe, Environ. Sci. Policy, 21, 35–49, 2012.
AMRA: Overview of intense rainfall on volcanic soils–regional and local scale, in: SafeLand – Living with landslide risk in Europe: Assessment, effects of global change, and risk management strategies, edited by: Crosta, G. B., Agliardi, F., Frattini, P., and Sosio, R., 101–122, 2012.
Berti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., and Pizziolo, M.: Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach, J. Geophys. Res., 117, F04006,, 2012.
Short summary
A model for predicting the timing of activation of rainfall-induced landslides is presented. Calibration against real events is based on genetic algorithms, and provides a family of optimal solutions (kernels) that maximize a fitness function. Accordingly, a set of mobility functions is obtained through convolution with rainfall. Once properly validated, the model allows one to estimate future landslide activations in the same study area, by employing either recorded or forecasted rainfall.