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

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

doi:10.3390/w13040488

Mariano Di Napoli, Andrea Cevasco + Show 6 more

Open Access

https://doi.org/10.3390/w13040488

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Journal: Water	Publication Date: Feb 13, 2021
Citations: 33	License type: CC BY 4.0

Affiliation: University of Genoa, University of Naples Federico II

Abstract

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.

Highlights

An attempt was made to obtain an indication on the goodness and effectiveness of the LDTRS model
The predictive capability of the predisposing factors should be considered and factors with non-predictive value should not be included in the models, improving the performance of the prediction
Orange bars represent the number of landslides falling in each Landslide Detachment Susceptibility (LDS) classes, while the blue bars indicate the areal extension of the different LDS classes

Summary

Introduction

Rainfall-triggered landslides are notoriously dangerous phenomena capable of causing serious damage and a notable death toll as well as important economic losses all over the world. Shallow landslides usually occur on steep soil-mantled slopes [1,2,3,4,5,6] as a consequence of heavy or intense rainfalls and often may evolve into potentially catastrophic flow-like movements. The consequences of shallow landslides and flow-like phenomena are typically more dangerous in inhabited centers located at the foot of slopes, where the hydrographic network is well developed

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