Debris flows represent dangerous occurrences in many parts of the world. Several disasters are documented due to this type of fast-moving landslides; therefore, natural-hazard assessment of debris flows is crucial for safety of life and property.
To this aim, much current work is being directed toward developing geotechnical-hydraulic models for the evaluation of debris flow susceptibility. A common base for such current models is parameterization of background predisposing and triggering factors such as inherent characteristics of geo-materials, topography, landscape and vegetation cover, rainfall regime, human activities, etc.
The same factors are also taken into account in soil erosion prediction models. Consequently, it seems worth investigating the effectiveness of the soil erosion index as debris flows susceptibility indicator.
Development of Landslide Susceptibility Map of Croatia
To this aim, a logistic regression analysis was carried out between the erosion index assessed by means of the Revised Universal Soil Loss Equation RUSLE model and the inventory of debris flows that have occurred in an area in Sicily Southern Italy.
Model assumptions were verified and validated by means of a series of statistical tools. Different possible scenarios were also evaluated by considering hypothetical changes in soil erosion rate under different rain erosivity conditions.
Notwithstanding the rough approximations in model data collection, the outcomes appear encouraging. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Geol Tec Ambient — Google Scholar. J Hydrol — Island Arc — Aleotti P A warning system for rainfall-induced shallow failures. Eng Geol 73 3—4 — Agric Ecosyst Environ — J Map 8 2 — Nat Hazards Earth Syst Sci — Baeza C, Corominas J Assessment of shallow landslide susceptibility by means of multivariate statistical techniques.
Earth Surf Process Landf — Boll AIC — Nuova Bios, Italy, pp 71— Basile G, Panebianco M Experimental alert model for hydrogeological risk: a case study in Sicily.
Beven K Changing ideas in hydrology. The case of physically-based models. Geomorphology — Boellstorff D, Benito G Impacts of set-aside policy on the risk of soil erosion in central Spain. Brenning A Spatial prediction models for landslide hazards: review, comparison and evaluation. Caine N The rainfall intensity-duration control of shallow landslides and debris-flows. Geogr Ann 62A 1—2 — Landslides 11 1 — IVth Inter. Cannon S H Regional rainfall-threshold conditions for abundant debris-flow activity.
An inventory-based approach to landslide susceptibility assessment and its application to the Virginio River Basin, Italy.You are here: Home Publications By theme Landslide susceptibility and landslide hazard.
Implementing landslide path dependency in landslide susceptibility modelling Samia J. A review of statistically-based landslide susceptibility models Reichenbach, P. A review of statistically-based landslide susceptibility models. Earth-Science Reviews. Hazard and population vulnerability analysis: a step towards landslide risk assessment Franny G.
Model Dev. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model Haydar Y. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model.
Available online 9 November Lollino et al. GIS-based deterministic analysis of deep-seated slope stability in a complex geological setting Mergili M. Land use change scenarios and landslide susceptibility zonation: the Briga catchment test area Messina, Italy Reichenbach P.
Environmental Management, doi Natural Hazards and Earth System Sciences, 14, —, www. Geoscientific Model Development Discussion, 7,www. Geomorphology69— Scaling properties of rainfall induced landslides predicted by a physically based model Alvioli M. Geomorphology, doi Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach Raia S.
Geoscientific Model Development, 7, doi: Geomorphology, doi: In: K.
Landslide susceptibility and landslide hazard
Sassa et al. Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach Raia, S. Geoscientific Model Development Discussion, 6,doi Scienze e Lettere Editore Commerciale, Landslides, Introduction to special issue "Landslides: forecasting, hazard evaluation and risk mitigation" Parise M. Natural Hazards, Vol. Harmonised approaches for landslide susceptibility mapping in Europe.
In: Malet, J. Optimal landslide susceptibility zonation based on multiple forecasts Rossi M. Geomorphology, Vol. Preface - Methods and strategies to evaluate landslide hazard and risk Reichenbach P.
Natural Hazards and Earth System Sciences, 10, —, www. Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium Van Den Eeckhaut, M.
Landslide hazard assessment, vulnerability estimation and risk evaluation: an example from the Collazzone area central Umbria, Italy Guzzetti F. Geografia Fisica e Dinamica Quaternaria, Vol.Some areas are more prone to landslides due to numerous varieties of factors and these landslide susceptible areas are needed to be identified and classified. The Classified European Landslide Susceptibility map v1. Here, the Croatian segment of this map is evaluated, reviewed, updated and optimized—improved with regional data sets.
The basic input parameters in this, pure heuristic approach is the same as in ELSUS but the used criteria are adapted to this region and known landslide data. Input parameters included: slope gradient map, soil parent material map and land cover map.
Conference paper First Online: 04 January This is a preview of subscription content, log in to check access. Technical note, European soil portal, 3p Google Scholar. Guzzetti F Landslide hazard and risk assessment. In: Croatian. Varnes DJ Landslide hazard zonation: a review of principle and practice. International Association of Engineering Geology, vol 3. Croatian Geological Survey Zagreb Croatia. Personalised recommendations. Cite paper How to cite? ENW EndNote.
Buy options.The susceptibility evaluation methodology employed for the updated map ELSUS Version 2 presented in this paper is identical to the previous approach, and comprises the differentiation of the analyzed European area into seven climate-physiographical model zones, the use of a reduced set of spatial susceptibility predictors shallow subsurface lithology, slope angle, and land coverand model zone-specific heuristic spatial multicriteria evaluations SMCE for susceptibility mapping.
IHME lithology describes both consolidated and unconsolidated shallow geological materials over Europe and can be shown to have a higher significance for landslide susceptibility evaluation than the soil parent material derived from ESDB. Other improvements consist in the change of the mapping unit from 1 km to m grid size and the incorporation of terrains not covered by ELSUS version 1 e. However, the assessment still suffers from missing landslide information in many European terrains.
It can be suspected that more distributed landslides information in specific model zones will further enhance the accuracy of ELSUS in the future.
Location of Repository. OAI identifier: oai:publications. Suggested articles.As in the previous map, the methodology employed for the updated map ELSUS Version 2 comprises the division of the analyzed European area into seven climate-physiographic model zones, the use of a reduced set of spatial susceptibility predictors slope angle, shallow subsurface lithology, and land coverand specific heuristic spatial multicriteria evaluations SMCE of model zones for susceptibility mapping.
IHME lithology describes both consolidated and unconsolidated shallow geologic materials over Europe, showing a higher significance for landslide susceptibility assessment than the soil parent material above. Additionally, in version 2 the mapping unit cell size increases from 1 km to m and new areas such as Iceland, Cyprus, the Faroes, and the Shetlands are covered on the map. A further improvement is the geographic adjustment of the slope angle, lithology and land cover spatial susceptibility criteria to uniform coastline information derived from VMAP data now allowing for an area-wide susceptibility mapping of European coastal areas.
It can be assumed that more distributed landslide information in specific model zones will further improve the accuracy of ELSUS in the future. Location of Repository. OAI identifier: oai:publications.
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A lovely problem to have though.Invited sessions and a poster session are also part of the workshop. Partial travel awards will be awarded to select conference participants as priority will be given to senior graduate students, post-graduate, recent Ph. Ds, junior faculty, and under-represented groups. Stats Life Statistics in Action Blog A World Without Statistics Around the World in Statistics ASA Statistical Significance Series The World of Statistics Flyer The World of Statistics Video Statistics as a Career At Work With Statisticians What Fields Employ Statisticians.
This article may help you understand the concept of statistical significance and the meaning of the numbers produced by The Survey System. This article is presented in two parts. The second part provides more technical readers with a fuller discussion of the exact meaning of statistical significance numbers. In normal English, "significant" means important, while in Statistics "significant" means probably true (not due to chance).
A research finding may be true without being important. When statisticians say a result is "highly significant" they mean it is very probably true.
They do not (necessarily) mean it is highly important. Take a look at the table below. We want to know if people from different areas or who drive different types of vehicles give different answers to the question. We see some differences, but want to know if those differences are likely due to chance, because of the particular people we happened to interview, or whether the differences seen here likely reflect real differences in the entire population of people represented by our sample.
To answer this question we used a statistic called chi (pronounced kie like pie) square shown at the bottom of the table in two rows of numbers.
The top row numbers of 0. The meaning of these statistics may be ignored for the purposes of this article.
The second row contains values. These are the significance levels and are explained following the table. Significance levels show you how likely a pattern in your data is due to chance. The most common level, used to mean something is good enough to be believed, is.
However, this value is also used in a misleading way. Instead it will show you ". To find the significance level, subtract the number shown from one.
The RUSLE erosion index as a proxy indicator for debris flow susceptibility
For example, a value of ". In this table, there is probably no difference in purchases of gasoline X by people in the city center and the suburbs, because the probability is. In contrast the high significance level for type of vehicle (. The Survey System uses significance levels with several statistics. In all cases, the p value tells you how likely something is to be not true. If a chi square test shows probability of.
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