Shifting from traditional landslide occurrence modeling to scenario estimation with a "glass-box" machine learning.

Extreme rainfall events represent one of the main triggers of landslides. As climate change continues to reshape global weather patterns, the frequency and intensity of such events are increasing, amplifying landslide occurrences and associated threats to communities. In this contribution, we analyze relationships between landslide occurrence and extreme rainfall events by using a "glass-box" machine learning model, namely Explainable Boosting Machine. What sets these models as a "glass-box" technique is their exact intelligibility, offering transparent explanations for their predictions. We leverage these capabilities to model the landslide occurrence induced by an extreme rainfall event in the form of spatial probability (i.e., susceptibility). In doing so, we use the heavy rainfall event in the Misa River Basin (Central Italy) on September 15, 2022. Notably, we introduce a rainfall anomaly among our set of predictors to express the intensity of the event compared to past rainfall patterns. Spatial variable selection and model evaluation through random and spatial routines are incorporated into our protocol. Our findings highlight the critical role of the rainfall anomaly as the most important variable in modeling landslide susceptibility. Furthermore, we leverage the dynamic nature of such a variable to estimate landslide occurrence under different rainfall scenarios.

InSAR data for detection and modelling of overexploitation-induced subsidence: application in the industrial area of Prato (Italy).

Spaceborne-based monitoring for environmental purposes has become a well-established practice. The recent progress of synthetic aperture radar (SAR) sensors, including through the European Space Agency's (ESA) Sentinel-1 constellation, has enabled the scientific community to identify and monitor several geohazards, including subsidence ground deformations. A case study in the Tuscany Region, Italy, highlights the effectiveness of interferometric synthetic aperture radar (InSAR) in detecting abrupt increases in ground deformation rates in an industrial area of Montemurlo municipality. In this case, InSAR data enabled prompt identification of the phenomenon, supporting the authorities in charge of environmental management to thoroughly investigate the situation. First, an on-site validation was performed via field surveys confirming the presence of cracks and fissures on some edifices. Further analysis, including water pumping rates, settlement gauge and topographic levelling, corroborated the InSAR data's findings regarding vertical deformation. Integration of collected data allowed for spatial identification and assessment of the subsidence bowl and its source depth recognized by the remote sensing data. The Montemurlo case offers a procedural guideline for managing abrupt accelerations, identified by InSAR data in subsidence-prone areas due to fluid overexploitation. In fact, these data proved useful in helping local authorities responsible for hydrogeomorphological risk management. With the exacerbation of deformation issues in subsidence-prone regions due to climate change, early detection and monitoring of such phenomena are increasingly crucial, with InSAR data playing a central role in achieving this goal.

Laser scanner and UAV digital photogrammetry as support tools for cosmic-ray muon radiography applications: an archaeological case study from Italy.

The use of light detection and ranging technologies, i.e. terrestrial laser scanner (TLS), airborne laser scanner (ALS) and mobile laser scanner (MLS), together with the unmanned aerial vehicles digital photogrammetry (UAV-DP) and satellite data are proving to be fundamental tools to carry out reliable muographic measurement campaigns. The main purpose of this paper is to propose a workflow to correctly plan and exploit these types of data for muon radiography aims. To this end, a real case study is presented: searching for hidden tombs in the Etruscan necropolis of Palazzone (Umbria, Italy). A high-resolution digital elevation model (DEM) and three-dimensional models of the ground surface/sub-surface of the study area were created by merging data obtained using different survey methods to achieve the most accurate three-dimensional environment. Indeed, the simulated muon flux transmission used to infer relative transmission values, and the estimated density distribution, depends on the reliability of the three-dimensional reconstructed ground surface model. The aim of this study is to provide knowledge on the use of TLS and UAV-DP data and GPS-acquired points within the transmission-based muography process and how these data could improve or worsen the muon imaging results. Moreover, this study confirmed that muography applications require a multidisciplinary approach.

Seismic and thermal precursors of crater collapses and overflows at Stromboli volcano.

Lava overflows are highly hazardous phenomena that can occur at Stromboli. They can destabilize the crater area and the "Sciara del Fuoco" unstable slope, formed by several sector collapses, which can generate potentially tsunamigenic landslides. In this study, we have identified precursors of the October-November 2022 effusive crisis through seismic and thermal camera measurements. We analyzed the lava overflow on October 9, which was preceded by a crater-rim collapse, and the overflow on November 16. In both cases, seismic precursors anticipating the overflow onset have been observed. The analysis of the seismic and thermal data led to the conclusion that the seismic precursors were caused by an escalating degassing process from the eruptive vent, which climaxed with the overflows. Volcano deformation derived from ground-based InSAR and strainmeter data showed that inflation of the crater area accompanied the escalating degassing process up to the beginning of the lava overflows. The inflation of the crater area was especially evident in the October 9 episode, which also showed a longer seismic precursor compared to the November 16 event (58 and 40 min respectively). These results are important for understanding Stromboli's eruptive mechanisms and open a perspective for early warning of potentially dangerous phenomena.

Three-dimensional muon imaging of cavities inside the Temperino mine (Italy).

Muon radiography (muography) is an imaging technique based on atmospheric muon absorption in matter that allows to obtain two and three-dimensional images of internal details of hidden objects or structures. The technique relies on atmospheric muon flux measurements performed around and underneath the object under examination. It is a non-invasive and passive technique and thus can be thought of as a valid alternative to common prospecting techniques used in archaeological, geological and civil security fields. This paper describes muon radiography measurements, in the context of archaeological and geological studies carried out at the Temperino mine (LI, Tuscany, Italy), for the search and three-dimensional visualisation of cavities. This mine has been exploited since Etruscan times until recently (1973), and is now an active tourist attraction with public access to the tunnels. Apart from the archaeological interest, the importance of mapping the cavities within this mine lies in identifying the areas where the extraction ores were found and also in the safety issues arising from the tourist presence inside the mine. The three-dimensional imaging is achieved with two different algorithms: one involving a triangulation of two or more measurements at different locations; the other, an innovative technique used here for the first time, is based on the back-projections of reconstructed muon tracks. The latter requires only a single muographic data tacking and is to be preferred in applications where more than one site location can be difficult to access. Finally the quality of the three-dimensional muographic imaging was evaluated by comparing the results with the laser scan profiles obtained for some known cavities within the Temperino mine.

Integrating multiple information sources for landslide hazard assessment: the case of Italy.

Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to "landslide news". Analysis was applied to obtain information about temporal (2010-2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between "landslide news", traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events.

A methodological approach of QRA for slow-moving landslides at a regional scale.

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results.

Ten years of pluviometric analyses in Italy for civil protection purposes.

The concept of climate change has grown in recent decades, influencing the scientific community to conduct research on meteorological parameters and their variabilities. Research on global warming, as well as on its possible economic and environmental consequences, has spread over the last 20 years. Diffused changes in trends have been stated by several authors throughout the world, with different developments observed depending on the continent. Following a period of approximately 40 days of almost continuous rain that occurred from October to November 2019 across the Italian territory and caused several hazards (e.g., floods and landslides), a relevant question for decision-makers and civil protection actors emerged regarding the relative frequencies of given rainfall events in the Warning Hazard Zones (WHZs) of Italy. The derived products of this work could answer this question for both weather and hydrogeological operators thanks to the frequency and spatio-temporal distribution analyses conducted on 10-year daily rainfall data over the entire Italian territory. This work aspires to be an additional tool used to analyse events that have occurred, providing further information for a better understanding of the probability of occurrence and distribution of future events.

Spatial forecasting of seismicity provided from Earth observation by space satellite technology.

Understanding the controls on the distribution and magnitude of earthquakes is required for effective earthquake forecasting. We present a study that demonstrates that the distribution and size of earthquakes in Italy correlates with the steady state rate at which the Earth's crust moves. We use a new high-resolution horizontal strain rate (S) field determined from a very dense velocity field derived from the combination of Global Navigation Satellite System (GNSS) and satellite radar interferometry from two decades of observations. Through a statistical approach we study the correlation between the S and the magnitude of M ≥ 2.5 earthquakes that occurred in the same period of satellite observations. We found that the probability of earthquakes occurring is linked to S by a linear correlation, and more specifically the probability that a strong seismic event occurs doubles with the doubling of S. It also means that lower horizontal strain rate zone can have as large earthquakes as high horizontal strain rate zones, just with a reduced probability. The work demonstrates an independent and quantitative tool to spatially forecast seismicity.

Vulnerability Assessment of Buildings due to Land Subsidence using InSAR Data in the Ancient Historical City of Pistoia (Italy).

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E-W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

Analysis of the Influence of the GPS Errors Occurred While Collecting Electrode Coordinates on the Electrical Resistivity of Tumuli.

In archaeological applications the accurate reconstruction of buried structures is mandatory. Electrical resistivity tomography is widely used for this purpose. Nevertheless, resistivity errors could be generated by wrong placement of electrodes. Papers in the literature do not discuss the influence of errors connected with the electrode position location (GPS-error). In this paper the first results of a Monte Carlo simulation analysis of data acquired on a tumulus are presented. The main research questions were: (i) if it is correct to ignore the GPS-error collect, and (ii) if a minimum threshold, that significantly affect the inversion, exists. Results, obtained considering planimetric GPS-errors of about one third of the fixed electrode distances, show that the GPS-errors affect resistivity, but the generated errors/anomalies: (a) are lower than that obtained without considering the topography, and (b) are significant from a numerical point of view, but do not affect the interpretation, being compatible with the soil resistivity ranges.

Author Correction: Perspectives on the prediction of catastrophic slope failures from satellite InSAR.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Perspectives on the prediction of catastrophic slope failures from satellite InSAR.

We demonstrate the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) to identify precursors to catastrophic slope failures. To date, early-warning has mostly relied on the availability of detailed, high-frequency data from sensors installed in situ. The same purpose could not be chased through spaceborne monitoring applications, as these could not yield information acquired in sufficiently systematic fashion. Here we present three sets of Sentinel-1 constellation images processed by means of multi-interferometric analysis. We detect clear trends of accelerating displacement prior to the catastrophic failure of three large slopes of very different nature: an open-pit mine slope, a natural rock slope in alpine terrain, and a tailings dam embankment. We determine that these events could have been located several days or weeks in advance. The results highlight that satellite InSAR may now be used to support decision making and enhance predictive ability for this type of hazard.

Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites.

We present the continuous monitoring of ground deformation at regional scale using ESA (European Space Agency) Sentinel-1constellation of satellites. We discuss this operational monitoring service through the case study of the Tuscany Region (Central Italy), selected due to its peculiar geological setting prone to ground instability phenomena. We set up a systematic processing chain of Sentinel-1 acquisitions to create continuously updated ground deformation data to mark the transition from static satellite analysis, based on the analysis of archive images, to dynamic monitoring of ground displacement. Displacement time series, systematically updated with the most recent available Sentinel-1 acquisition, are analysed to identify anomalous points (i.e., points where a change in the dynamic of motion is occurring). The presence of a cluster of persistent anomalies affecting elements at risk determines a significant level of risk, with the necessity of further analysis. Here, we show that the Sentinel-1 constellation can be used for continuous and systematic tracking of ground deformation phenomena at the regional scale. Our results demonstrate how satellite data, acquired with short revisiting times and promptly processed, can contribute to the detection of changes in ground deformation patterns and can act as a key information layer for risk mitigation.

Displacement of a landslide retaining wall and application of an enhanced failure forecasting approach.

The 10-mile Slide is contained within an ancient earthflow located in British Columbia, Canada. The landslide has been moving slowly for over 40 years, requiring regular maintenance work along where a highway and a railway track cross the sliding mass. Since 2013, the landslide has shown signs of retrogression. Monitoring prisms were installed on a retaining wall immediately downslope from the railway alignment to monitor the evolution of the retrogression. As of September 2016, cumulative displacements in the horizontal direction approached 4.5 m in the central section of the railway retaining wall. After an initial phase of acceleration, horizontal velocities showed a steadier trend between 3 and 9 mm/day, which was then followed by a second acceleration phase. This paper presents an analysis of the characteristics of the surface displacement vectors measured at the monitoring prisms. Critical insight on the behavior and kinematics of the 10-mile Slide retrogression was gained. An advanced analysis of the trends of inverse velocity plots was also performed to assess the potential for a slope collapse at the 10-mile Slide and to obtain further knowledge on the nature of the sliding surface.

Shifts in the eruptive styles at Stromboli in 2010-2014 revealed by ground-based InSAR data.

Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) is an efficient technique for capturing short, subtle episodes of conduit pressurization in open vent volcanoes like Stromboli (Italy), because it can detect very shallow magma storage, which is difficult to identify using other methods. This technique allows the user to choose the optimal radar location for measuring the most significant deformation signal, provides an exceptional geometrical resolution, and allows for continuous monitoring of the deformation. Here, we present and model ground displacements collected at Stromboli by GBInSAR from January 2010 to August 2014. During this period, the volcano experienced several episodes of intense volcanic activity, culminated in the effusive flank eruption of August 2014. Modelling of the deformation allowed us to estimate a source depth of 482 ± 46 m a.s.l. The cumulative volume change was 4.7 ± 2.6 × 10(5) m(3). The strain energy of the source was evaluated 3-5 times higher than the surface energy needed to open the 6-7 August eruptive fissure. The analysis proposed here can help forecast shifts in the eruptive style and especially the onset of flank eruptions at Stromboli and at similar volcanic systems (e.g. Etna, Piton de La Fournaise, Kilauea).

Analysis of building deformation in landslide area using multisensor PSInSAR™ technique.

Buildings are sensitive to movements caused by ground deformation. The mapping both of spatial and temporal distribution, and of the degree of building damages represents a useful tool in order to understand the landslide evolution, magnitude and stress distribution. The high spatial resolution of space-borne SAR interferometry can be used to monitor displacements related to building deformations. In particular, PSInSAR technique is used to map and monitor ground deformation with millimeter accuracy. The usefulness of the above mentioned methods was evaluated in San Fratello municipality (Sicily, Italy), which was historically affected by landslides: the most recent one occurred on 14th February 2010. PSInSAR data collected by ERS 1/2, ENVISAT, RADARSAT-1 were used to study the building deformation velocities before the 2010 landslide. The X-band sensors COSMO-SkyMed and TerraSAR-X were used in order to monitor the building deformation after this event. During 2013, after accurate field inspection on buildings and structures, damage assessment map of San Fratello were created and then compared to the building deformation velocity maps. The most interesting results were obtained by the comparison between the building deformation velocity map obtained through COSMO-SkyMed and the damage assessment map. This approach can be profitably used by local and Civil Protection Authorities to manage the post-event phase and evaluate the residual risks.