Impact of landslides on transportation routes during the 2016–2017 Central Italy seismic sequence

Martino, Salvatore; Bozzano, Francesca; Caporossi, Paolo; D’Angiò, Danilo; Seta, Marta Della; Esposito, Carlo; Fantini, Andrea; Fiorucci, Matteo; Giannini, Leonardo Maria; Iannucci, Roberto; Marmoni, Gian Marco; Mazzanti, Paolo; Missori, C.; Moretto, Serena; Piacentini, Daniela; Rivellino, Stefano; Romeo, Roberto; Sarandrea, Paolo; Schilirò, Luca; Troiani, Francesco; Varone, Chiara

doi:10.1007/s10346-019-01162-2

Cited by 42 publications

(32 citation statements)

References 60 publications

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“…Martino et al (2014) defined a similar curve for Italy (the CEDIT curve) calibrated taking into account rock fall and disrupted landslides induced by earthquakes occurred starting from 1908 (Reggio and Messina earthquake) until 2012 (Emilia earthquake) and geolocalised with greater precision than those further away in time. The maximum distances of earthquake-induced effects surveyed with the use of GPS immediately after the seismic sequence of central Italy in 2016-2017 (Martino et al, 2019) and after the Montecilfone earthquake in 2018 (Martino et al, 2020a) well respected the maximum epicentral distance for disrupted landslides defined for Italy (CEDIT curve). In this way, the Input dataset respects the CEDIT curve and can thus be considered as a reliable dataset to train the neural network (see Figure 3).…”

Section: Cedit Cataloguementioning

confidence: 91%

“…The EQtLs susceptibility model we built in this study is based on data collected in the CEDIT (Italian acronym of "Italian database of earthquake-triggered ground failures") catalogue (Martino et al, 2014). This catalogue contains records of several ground effects triggered by earthquakes within the Italian territory from 1117 d.C. to August 16 th 2018, when the M w 5.1 Montecilfone earthquake occurred in the Molise region (Prestininzi and Romeo, 2000;Fortunato et al, 2012;Martino et al, 2019Martino et al, , 2020a. The latest release of the catalogue (Caprari et al, 2018) is available online at (link here), and consists of a relational geodatabase in which each earthquake is associated with all the multiple ground-failure effects induced by it.…”

Section: Cedit Cataloguementioning

confidence: 99%

“…These account for fully nonlinear soil properties' behaviour during the seismic shaking (Rathje and Bray, 2000) and they also consider the probabilistic variation of seismic input properties (Bray and Travasarou, 2007). Based on more sophisticated computational approaches, which are comprehensive of different landslide mechanisms and methods for slope stability analysis, probability maps of expected Newmark's displacements can be obtained at a regional scale through the recently proposed PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake-Induced slope FAiLures) approach (Esposito et al, 2016;Martino et al, 2018Martino et al, , 2019. PARSIFAL considers both landslide susceptibility maps and landslide inventories for detecting slope areas prone to landslides, to compute probability of EQtLs occurrence, based on distributions of Newmark's displacement values related to an input subset.…”

Section: Introductionmentioning

confidence: 99%

“…However, the algorithmic architecture one chooses to implement has notable repercussions on the performance each model provides. For instance, simple bivariate statistical models provide quite straightforward interpretation of the functional relations existing between factors and landslides (e.g., Weight of Evidence; Bonham-Carter, 1989;Van Westen, 2002;Martino et al, 2019). But, this is achieved at the expense of the statistical rigor (the model does not assume any underlying probability distribution nor the interaction among explanatory variables) and performances, which are usually superseded by more complex statistical tools.…”

Section: Introductionmentioning

confidence: 99%

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Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Amato¹,

Fiorucci²,

Martino³

et al. 2021

Preprint

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The use of Artificial Neural Network (ANN) approaches has gained a significant role over the last decade in the field of predicting the distribution of effects triggered by natural forcing, this being particularly relevant for the development of adequate risk mitigation strategies. Among the most critical features of these approaches, there are the accurate geolocation of the available data as well as their numerosity and spatial distribution. The use of an ANN has never been tested at a national scale in Italy, especially in estimating earthquake-triggered landslides susceptibility. Based on the statistics deductible from the most up-to-date national inventory of earthquake-induced ground effects, i.e. the CEDIT catalogue, it results that over 56% of the ground effects triggered by earthquakes in Italy are represented by landslides. Therefore, a landslide dataset with such high geolocation precision was suitable to evaluate the efficiency of an ANN to explain the distribution of landslides over the Italian territory. An ex-post evaluation of the ANN-based susceptibility model was also performed, using a sub-dataset of historical data with lower geolocation precision. The ANN training highly performed in terms of spatial prediction, by partitioning the Italian landscape into slope units. The obtained results returned a distribution of potentially unstable slope units with maximum concentrations primarily distributed in the central-northern Apennines and secondarily in the southern Apennines. Moreover, the Alpine sector clearly appeared to be divided into two areas, a western one with relatively low susceptibility to earthquake-triggered landslides and the eastern sector with a higher susceptibility. However, the scale of the analysis carried out to train the ANN does not allow it to be applied for planning purposes or for seismic microzonation studies, for which training on a smaller spatial scale will be required.

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Section: Cedit Cataloguementioning

confidence: 91%

Section: Cedit Cataloguementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Amato¹,

Fiorucci²,

Martino³

et al. 2021

Preprint

Self Cite

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“…Moreover, Italy is a country strongly affected by natural hazards, such as landslides [1], which often are cause of deaths, injured and physical damage to private and public properties. Among these, slow-moving landslides, generating deformation, cracks and local failures to road surface, are responsible for considerable economic losses [2].…”

Section: Introductionmentioning

confidence: 99%

Integrated Procedure for Monitoring and Assessment of Linear Infrastructures Safety (I-Pro MONALISA) Affected by Slope Instability

Infante¹,

Martire

Calcaterra

et al. 2019

Applied Sciences

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The occurrence of geological events such as landslides is one of the main causes of damage along linear infrastructures: Damage to transport infrastructures, as roads, bridges, and railways, can restrict their optimal functions and contribute to traffic accidents. The frequent and accurate monitoring of slope instability phenomena and of their interaction with existing man-made infrastructures plays a key role in risk prevention and mitigation activities. In this way, the use of high-resolution X-band synthetic aperture radar (SAR) data, characterized by short revisiting times, has demonstrated to be a powerful tool for a periodical noninvasive monitoring of ground motion and superstructure stability, aimed at improving the efficiency of inspection, repairing, and rehabilitation efforts. In the present work, we suggest a semiautomatic GIS approach, which, by using satellite radar interferometry data and results of geomorphological field survey integrated in a qualitative vulnerability matrix, allows to identify sections with different levels of damage susceptibility, where detailed conventional in situ measurements are required for further analysis. The procedure has been tested to investigate landslide-induced effects on a linear infrastructure in Campania Region (Italy), the Provincial Road “P.R. 264”, which is affected, along its linear development, by several slope instabilities. COSMO-SkyMed interferometric products, as indicator of ground kinematics, and results of in situ damage survey, as indicator of consequences, have been merged in a qualitative 4 × 4 matrix, thus obtaining a vulnerability zoning map along a linear infrastructure in January 2015. Furthermore, an updating of landslide inventory map is provided: In addition to 24 official landslides pre-mapped in 2012, 30 new events have been identified, and corresponding intensity and state of activity has been detected.

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The ICL Journal Landslides—16 Years of Capacity Development for Landslide Risk Reduction

Mikoš

Sassa²,

Arbanas

2020

Understanding and Reducing Landslide Disaster Risk

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Impact of landslides on transportation routes during the 2016–2017 Central Italy seismic sequence

Cited by 42 publications

References 60 publications

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Integrated Procedure for Monitoring and Assessment of Linear Infrastructures Safety (I-Pro MONALISA) Affected by Slope Instability

The ICL Journal Landslides—16 Years of Capacity Development for Landslide Risk Reduction

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