Multidisciplinary Study of Subsidence and Sinkhole Occurrences in the Acque Albule Basin (Roma, Italy)

Ritis, R. De; Nardi, Adriano; Materni, Valerio; Venuti, A.; Stefanelli, P.; Rotella, Giovanni; Sapia, Vincenzo; Carpentieri, Elena; Tolomei, Cristiano; Civico, Riccardo; Coltorti, Massimo; Nanni, Torquato; Argentieri, Alessio; Gaudio, P. Del; Chiappini, M.

doi:10.1029/2019ea000870

Cited by 2 publications

(4 citation statements)

References 45 publications

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“…InSAR ground motion rates show poor correlation with potential sinkhole development: the response curve is quite noisy, even if it records a peak for vertical velocities between À10 and À5 mm/year. Basically, the deformation measured by satellite radar data confirmed the subsiding movements historically reported in the area even by past InSAR data (Nisio et al 2007;Ciotoli et al 2016;De Ritis et al 2020).…”

Section: Susceptibility Zoning Mapsupporting

confidence: 78%

“…travertine, but actually by the rock mechanical conditions. In this regard, previous works (De Ritis et al 2020) carried out detailed geophysical-geotechnical studies on the area, and correlated ground surface variations (undulations, small sinkholes and fractures) with the presence of disarticulated travertine blocks in the stratigraphical sequences.…”

Section: Discussionmentioning

confidence: 99%

“…Buried collapse and deep piping sinkholes filled with soil and regolith are common in the travertine substratum of this geothermal-seismic area. Moreover, travertine quarry activities in the area and the load of buildings due to urbanization accelerate the development of subsurface cavities in the travertine plateau (De Ritis et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy

Bianchini

Confuorto

Intrieri

et al. 2022

Geocarto International

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This work presents a sinkhole susceptibility and risk assessment mapping in Guidonia-Bagni di Tivoli plain (Italy), a travertine sinkhole-prone area where sudden occurrences of sinkholes have happened in past and recent times. We collected a point-like sinkhole inventory and we considered a series of different sinkholecontrolling and precursory factors over the study area, related to its geo-litho-hydrological setting and to its terrain deformational scenario, i.e. ground motion rates derived from InSAR COSMO-SkyMed imagery. A sinkhole susceptibility map was produced through a machine learning model, namely Maximum Entropy algorithm (MaxEnt). Results highlight that the most determining factors for sinkhole formation are the lithology, the travertine thickness, groundwater and the land use. The sinkhole susceptibility map was then combined with data on vulnerability and elements-at-risk economic exposure in order to provide a sinkhole risk map of the area. The outcomes show that areas at higher risk covers about 2% of the total study area and primarily relies on the zoning of the main urban fabric. In particular, it is worth to highlight that 5% of the whole road-network pavement and 27% of all the residential buildings fall into High and Very High risk classes. Overall, results of this work demonstrate capabilities of machine learning models to assess sinkhole susceptibility for predicting potential sinkhole areas, and provide a sinkhole risk map, along with information on urban environment, as a useful tool for urban planning and geohazard risk management.

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Section: Susceptibility Zoning Mapsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

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Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy

Bianchini

Confuorto

Intrieri

et al. 2022

Geocarto International

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“…Different surface and subsurface investigation methods to identify and characterize sinkholes have been applied throughout the world. Among the others, we can report aerial and satellite image analysis (Dou et al., 2015; Festa et al., 2012; Gutiérrez et al., 2011; Panno & Luman, 2013), topographic map analysis (Basso et al., 2013; Brinkmann et al., 2008; Gutiérrez et al., 2011), field surveys (Bruno et al., 2008; Gutiérrez et al., 2007), LiDAR (Kim et al., 2019; Zhu et al., 2014, 2020), InSAR (Baer et al., 2018; Busetti et al., 2020; Galve et al., 2015; Guerrero et al., 2021; Intrieri et al., 2015; Jones & Blom, 2014; Malinowska et al., 2019; Oliver‐Cabrera et al., 2020; Orhan et al., 2021; Shi et al., 2019; Solari et al., 2020; Theron et al., 2017), ground‐based monitoring (Desir et al., 2018; Kersten et al., 2017; Kobe et al., 2019; Sevil et al., 2017; Zhende et al., 2013), geophysical surveys (De Ritis et al., 2020; García‐Moreno & Mateos, 2011; Krawczyk et al., 2012; Kühn et al., 2011; Margiotta et al., 2012; Malehmir et al., 2016; Pazzi et al., 2018; Ronen et al., 2019; Samyn et al., 2014; Stierman, 2004; Waltham et al., 2005; Wust‐Bloch & Joswig, 2006), hydrogeochemical studies (Delkhahi et al., 2020; Taheri et al., 2021) and trenching (Carbonel et al., 2015; Gutiérrez et al., 2018; Sevil et al., 2017) have been used in a series of multidisciplinary and multi‐technique investigations.…”

Section: Introductionmentioning

confidence: 99%

A joint geophysical approach to tune an integrated sinkhole monitoring method in evaporitic environments

Calligaris

Forte

Busetti

et al. 2023

Near Surface Geophysics

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Several methodologies allow for the detection and mapping of existing sinkholes in order to asses and manage the associated hazards and risks. These phenomena, linked to the presence of soluble rocks, are well known globally as they can cause severe damage to man‐made structures. In this paper, we propose an integrated method applied to a test‐site area in NE Italy where, on May 11 2017, a failure shaped like a sinkhole, suddenly occurred along a main regional road, which then had to be closed to traffic in part as a result of a landslide developing on the slope just upstream from the surface depression which had already formed. The slope was reprofiled, a paved barrier was placed at the toe of the slope, and the road itself was finally repaired and restored. In the test site, a detailed morphological and geological survey was performed, as well as several integrated multi‐scale geophysical investigations, both in correspondence to the sinkhole location and in surrounding areas where other depressions were found. Results confirm the absence of large cavities down to the maximum investigated depth and highlighted a complex geological situation with abrupt lateral variations, a straight correlation between different geomorphological and geological elements, and the role of water paths. Geophysical investigations were found to be a useful tool to monitor the future evolution of the identified phenomena and to prevent further collapses and disasters along roads.

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Multidisciplinary Study of Subsidence and Sinkhole Occurrences in the Acque Albule Basin (Roma, Italy)

Abstract: Capitale-Dipartimento VI "Pianificazione territoriale generale"-Servizio 3 "Geologico, difesa del suolo e protezione civile in ambito metropolitano",

Cited by 2 publications

References 45 publications

Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy

Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy

A joint geophysical approach to tune an integrated sinkhole monitoring method in evaporitic environments

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