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Using Field-Based Monitoring to Enhance the Performance of Rainfall Thresholds for Landslide Warning
Open AccessArticle

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

1
Department of Earth, Environment and Life Sciences, University of Genoa, Corso Europa, 26 16132 Genoa, Italy
2
Department of Earth, Environment and Resources Sciences, Federico II University of Naples, Complesso Universitario di Monte Sant’Angelo, Via Cinthia, 21 80126 Naples, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Roy C. Sidle
Water 2021, 13(4), 488; https://doi.org/10.3390/w13040488
Received: 28 December 2020 / Revised: 11 February 2021 / Accepted: 11 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)
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 applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed. View Full-Text
Keywords: shallow landslides; susceptibility mapping; machine learning; runout modeling; GIS-based approach; reach angle; Cinque Terre National Park shallow landslides; susceptibility mapping; machine learning; runout modeling; GIS-based approach; reach angle; Cinque Terre National Park
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MDPI and ACS Style

Di Napoli, M.; Di Martire, D.; Bausilio, G.; Calcaterra, D.; Confuorto, P.; Firpo, M.; Pepe, G.; Cevasco, A. Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches. Water 2021, 13, 488. https://doi.org/10.3390/w13040488

AMA Style

Di Napoli M, Di Martire D, Bausilio G, Calcaterra D, Confuorto P, Firpo M, Pepe G, Cevasco A. Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches. Water. 2021; 13(4):488. https://doi.org/10.3390/w13040488

Chicago/Turabian Style

Di Napoli, Mariano; Di Martire, Diego; Bausilio, Giuseppe; Calcaterra, Domenico; Confuorto, Pierluigi; Firpo, Marco; Pepe, Giacomo; Cevasco, Andrea. 2021. "Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches" Water 13, no. 4: 488. https://doi.org/10.3390/w13040488

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