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Heuristic Method for Landslide Susceptibility Assessment in the Messina Municipality

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Engineering Geology for Society and Territory - Volume 2

Abstract

October 1st 2009 a heavy rainfall caused more than one thousand debris flows in two small basins south of Messina town (North–East Sicily, Italy). After the disaster Messina Municipality entrusted the risk assessment of its whole territory to a geomorphology and GIS team, led by ENEA, that applied and improved a heuristic method aimed at the identification of the areas prone to landslide triggering, based on the recognition of physical and dynamical characteristics of phenomena. This landslide susceptibility method is a GIS based process that consists in four steps: Field Survey, Site Analysis, Macro-Area Analysis and Susceptibility Analysis. Through the Field Survey of natural and anthropic conditions of past and recent phenomena a landslide inventory is generated, and each thematic map is stored in a GIS database. In the Site Analysis a univariate statistical analysis of the inventory leads to classify each causative factor as a discriminating parameter (condition necessary for slope instability) or as a predisposing factor (condition that works together in worsening slope stability). In the Macro-Area Analysis the GIS overlay of all thematic maps is performed to recognize, in surrounding areas, features similar to those of past events. Finally a susceptibility function runs the weighted sum of Predisposing Factors in zones where all Discriminating Parameters are present, deriving the Susceptibility Map. The here discussed heuristic GIS method allows the integration of multidisciplinary knowledge, both quantitative and qualitative, thus exploiting field experience.

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References

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Correspondence to Gabriele Leoni .

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Leoni, G. et al. (2015). Heuristic Method for Landslide Susceptibility Assessment in the Messina Municipality. In: Lollino, G., et al. Engineering Geology for Society and Territory - Volume 2. Springer, Cham. https://doi.org/10.1007/978-3-319-09057-3_82

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