12 research outputs found
PRELIMINARY RESULTS ON THE COMPARISON BETWEEN EMPIRICAL AND PHYSICALLY-BASED RAINFALL THRESHOLDS FOR SHALLOW LANDSLIDES OCCURRENCE
Rainfall induced shallow landslides are hazardous
phenomena causing significant damages all over the world.
Rainfall thresholds are the most used tool to predict the
occurrence of such instabilities over large areas. Most of these
thresholds are empirical, basing on past rainfall events
triggered landslides. These present several limitations, due to
the amount and the uncertainty of available data and neglecting
antecedent soil hydrological conditions. Physically-based
thresholds could take into account also for soil hydrological
responses towards rainfalls. Thus, this work aimed to compare
both these typologies of thresholds, reconstructed for a 250 km2
area of northern Italian Apennines prone to shallow
landsliding. Preliminary results of this research show the
significant differences between thresholds reconstructed by
means of different approaches. Empirical threshold estimates a
lower triggering cumulated amount of rainfall than the values
for physically-based thresholds, considering the same duration
of rainfall event. Antecedent pore water pressure conditions
have significant effects on reconstructed threshold, reducing
the amount of rainfall leading to instabilities according to
conditions approaching soil complete saturation. These
preliminary results will be improved in order to create
threshold useful for early warning strategies
A data-drive model for the assessment of shallow landslides hazard with the integration of satellite soil moisture and rainfall data
R ainfall-induced shallow landslides are very dangerous phenomena, widespread all over the
world, which could provoke significant damages to buildings, roads, facilities, cultivations and,
sometimes, loss of human lives. For these reasons, it is necessary assessing the most prone zones
in a territory which is particularly susceptible to these phenomena and the frequency of the
triggering events, according to the return time of them, which generally correspond to intense and
concentrated rainfalls. The most adopted methodologies for the determination of the
susceptibility and hazard of a territory are physically-based models, that quantify the hydrological
and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas,
these methodologies could be applied in a reliable way in little catchments, where geotechnical
and hydrological features of the materials affected by shallow failures are homogeneous. Datadriven
models could constraints these, even if they are generally built up taking into only the
predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a
territory, without considering the frequency of the triggering events. It is then required to consider
also triggering factors of shallow landslides to allow these methods to estimate also the
probability of occurrence and, then, the hazard. This work presents the development and the
implementation of data-driven model able to assses the spatio-temporal probability of occurrence
of shallow landslides in large areas by means of a data-driven technique. The model is based on
Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological,
geological and land use predisposing factors to triggering factors of shallow failures. These
triggering factors correspond to soil saturation degree and rainfall amounts, which are available
for entire a study area thanks to satellite measures. The methodological approach is testing in
30-40 km2 wide catchments of Oltrepò Pavese hilly area (northern Italy), where detailed
inventories of shallow landslides occurred during past triggering events and corresponding
satellite soil moisture and rainfall maps are available. This work was made in the frame of the
ANDROMEDA project, funded by Fondazione Cariplo
Empirical and Physically Based Thresholds for the Occurrence of Shallow Landslides in a Prone Area of Northern Italian Apennines
Rainfall thresholds define the conditions leading to the triggering of shallow landslides
over wide areas. They can be empirical, which exploit past rainfall data and landslide inventories, or
physicallybased, which integrate slope physical–hydrological modeling and stability analyses. In this
work, a comparison between these two types of thresholds was performed, using data acquired in
Oltrepò Pavese (Northern Italian Apennines), to evaluate their reliability. Empirical thresholds were
reconstructed based on rainfalls and landslides triggering events collected from 2000 to 2018. The same
rainfall events were implemented in a physicallybased model of a representative testsite, considering
dierent antecedent pore-water pressures, chosen according to the analysis of hydrological monitoring
data. Thresholds validation was performed, using an external dataset (August 1992–August 1997).
Soil hydrological conditions have a primary role on predisposing or preventing slope failures. In
Oltrepò Pavese area, cold and wet months are the most susceptible periods, due to the permanence of
saturated or close-to-saturation soil conditions. The lower the pore-water pressure is at the beginning
of an event, the higher the amount of rain required to trigger shallow failures is. physicallybased
thresholds provide a better reliability in discriminating the events which could or could not trigger
slope failures than empirical thresholds. The latter provide a significant number of false positives,
due to neglecting the antecedent soil hydrological conditions. These results represent a fundamental
basis for the choice of the best thresholds to be implemented in a reliable earlywarning system
The neural network associated with lexical-semantic knowledge about social groups.
A person can be appraised as an individual or as a member of a social group. In the present study we tested whether the knowledge about social groups is represented independently of the living and non-living things. Patients with frontal and temporal lobe tumors involving either the left or the right hemisphere performed three tasks - picture naming, word-to-picture matching and picture sorting - tapping the lexical semantic knowledge of living things, non-living things and social groups. Both behavioral and voxel-based lesion-symptom mapping (VLSM) analyses suggested that social groups might be represented differently from other categories. VLSM analysis carried out on naming errors revealed that left-lateralized lesions in the inferior frontal gyrus, amygdala, insula and basal ganglia were associated with the lexical-semantic processing of social groups. These findings indicate that the social group representation may rely on areas associated with affective processing