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Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches by Di Napoli, Mariano (author);Di Martire, Diego (author);Bausilio, Giuseppe (author);Calcaterra, Domenico (author);Confuorto, Pierluigi (author);Firpo, Marco (author);Pepe, Giacomo (author);Cevasco, Andrea (author) is a Environmental Science article available to read on EtoBox.

What is Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches about?

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and apply

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Author

Di Napoli, Mariano (author);Di Martire, Diego (author);Bausilio, Giuseppe (author);Calcaterra, Domenico (author);Confuorto, Pierluigi (author);Firpo, Marco (author);Pepe, Giacomo (author);Cevasco, Andrea (author)

Publisher

MDPI AG

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)