Deep learning forecast of rainfall-induced shallow landslides.

Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides.

Data-driven flood hazard zonation of Italy.

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models.

Implications of climate change on landslide hazard in Central Italy.

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy.

The Vulnerability of People to Damaging Hydrogeological Events in the Calabria Region (Southern Italy).

Damaging Hydrogeological Events (DHEs) are severe weather periods during which floods, landslides, lightning, windstorms, hail or storm surges can harm people. Climate change is expected to increase the frequency/intensity of DHEs and, consequently, the potential harm to people. We investigated the impacts of DHEs on people in Calabria (Italy) over 37 years (1980-2016).

LAND-deFeND - An innovative database structure for landslides and floods and their consequences.

Information on historical landslides and floods - collectively called "geo-hydrological hazards - is key to understand the complex dynamics of the events, to estimate the temporal and spatial frequency of damaging events, and to quantify their impact. A number of databases on geo-hydrological hazards and their consequences have been developed worldwide at different geographical and temporal scales. Of the few available database structures that can handle information on both landslides and floods some are outdated and others were not designed to store, organize, and manage information on single phenomena or on the type and monetary value of the damages and the remediation actions.

Gender, age and circumstances analysis of flood and landslide fatalities in Italy.

Floods and landslides are frequent and destructive geo-hydrological hazards that cause harm to people every year. We analysed data on 1292 landslide and 771 flood fatalities that occurred in Italy in the 50-year period 1965-2014, to determine the dependence of the fatalities on gender and age and the circumstances of death by type of hazard. The multinomial probability mass function of the expected fatalities by gender and age, as reported by national census data, were estimated and compared with the observed landslide and flood fatalities.

Forecasting natural hazards, performance of scientists, ethics, and the need for transparency.

Landslides are one of several natural hazards. As other natural hazards, landslides are difficult to predict, and their forecasts are uncertain. The uncertainty depends on the poor understanding of the phenomena that control the slope failures, and on the inherent complexity and chaotic nature of the landslides.

Landslide vulnerability criteria: a case study from Umbria, central Italy.

Little is known about the vulnerability to landslides, despite landslides causing frequent and widespread damage to the population and the built-up environment in many areas of the world. Lack of information about vulnerability to landslides limits our ability to determine landslide risk. This paper provides information on the vulnerability of buildings and roads to landslides in Umbria, central Italy.

Evaluation of flood and landslide risk to the population of Italy.

We have compiled a database of floods and landslides that occurred in Italy between AD 1279 and 2002 and caused deaths, missing persons, injuries, and homelessness. Analysis of the database indicates that more than 50,593 people died, went missing, or were injured in 2580 flood and landslide events. Harmful events were inventoried in 26.

Rockfall hazard and risk assessment along a transportation corridor in the Nera Valley, central Italy.

Rockfalls are a common type of fast-moving slope failures, and in many countries they represent the primary cause of landslide fatalities. We present a methodology to ascertain rockfall hazard and to determine the associated risk along transportation networks. The proposed methodology is based on the combined analysis of the recurrence of rockfall events, determined from historical information, the frequency-volume statistics of rockfalls, obtained from inventories of recent rockfall triggering events, and the results of a physically based, spatially distributed rockfall simulation model used to determine rockfall hazard.

Self-organization, the cascade model, and natural hazards.

We consider the frequency-size statistics of two natural hazards, forest fires and landslides. Both appear to satisfy power-law (fractal) distributions to a good approximation under a wide variety of conditions. Two simple cellular-automata models have been proposed as analogs for this observed behavior, the forest fire model for forest fires and the sand pile model for landslides.