1,720,996 research outputs found
Planning and Monitoring of Mitigation Measures in a Landslide-Affected Area Through Numerical Modelling, Conventional and Satellite Data
No full textDifferential SAR interferometry technique for control of linear infrastructures affected by ground instability phenomena
No full textMachine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy
No full textThis 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 sinkhole-controlling 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
Monitoring of Strategic Buildings in Civil Protection Activities via Remote Sensing Data
No full textPS-driven inventory of town-damaging landslides in the Benevento, Avellino and Salerno Provinces, southern Italy
No full textMachine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence
Full text linkThe continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Application of a statistical approach to landslide susceptibility map generation in urban settings
No full textLandslide susceptibility maps are effective tools for the mitigation of risks caused by such geological events. In line with recent scientific trends and thanks to the availability of detailed geological data, landslide susceptibility modeling, by means of statistical methodologies, has gained increasing consideration. The present work is based on a methodology widely employed in the field of ecology to draw prediction maps for the occurrence probability of certain species (MaxEnt). The study area is located in Palma Campania, a town sited in the
peri-vesuvian area (in the province of Napoli, southern Italy) and characterized by a significant presence of pyroclastic soils, affected by several landslide events, one of which killed eight people in 1986. In this work, eleven geomorphological and geological predisposing factors were selected, based on previous experiences of landslides in peri-vesuvian areas and following several field surveys. Results were critically evaluated using a validation dataset (Receiver Operating Characteristic—ROC curves), by means of Sensitivity-Specificity graphs estimating Area Under Curve (AUC), and other tests such as the Jackknife and response curves, which highlighted the major role played by a number of factors. The consistent agreement between our results and the existing official map demonstrates the validity of the adopted procedure for emergency and land planning
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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