<sup>GA</sup><i>SAKe</i>: forecasting landslide activations by a genetic-algorithms-based hydrological model

Abstract

Abstract. GA SAKe 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 GA SAKe 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.

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

“…The same coupling approach may be used with other recently proposed empirical models, such as GA-SAKe (Terranova et al, 2015).…”

“…Few examples of real-time predictions of the probability of triggering of rainfall-induced landslides in a small area (i.e., a slope or a small catchment) can be found in the literature (e.g., Sirangelo and Versace, 1996;Sirangelo and Braca, 2004;Schmidt et al, 2008;Greco et al, 2013;Capparelli et al, 2013;Terranova et al, 2015;Manconi and Giordan, 2016;Ozturk et al, 2016). This is due to the intrinsic difficulty of finding historical data sets of rainstorms and corresponding landslides occurring in a small area, with enough data to allow reliable estimation of the probability of landslide triggering during extreme (and thus rare) rainfall events.…”

Geomorphic effects caused by heavy rainfall in southern Calabria (Italy) on 30 October–1 November 2015

“…As a way of investigating the amounts of rain deemed responsible for the observed types of ground effects, and considering the extent of the affected basins, the maximum amounts of UK cumulative rainfall in 24 h between t 1 and t 2 , h max (24), were also extracted for each cell of the domain. In fact, shallow landslides, flash floods and slope erosion commonly result strongly related to high intensity, short duration rainfall events (Cascini & Versace, 1986, 1988Terranova & Gariano, 2014;Terranova, Gariano, Iaquinta, & Iovine, 2015).…”

Post-Landslide Liquefaction Analysis: A Case Study in the Kupang Regency Area, Indonesia

“…Terranova et al [25] developed a hydrological model based on genetic algorithms to predict landslide activations, showcasing its utility in forecasting and providing early warnings for civil protection. Similarly, Santangelo et al [26] demonstrated the efficiency of remote landslide mapping techniques using laser rangefinder binoculars and GPS, proving that geographical data gathered remotely is comparable to that collected through on-ground GPS surveys.…”