1,721,075 research outputs found
Analysis of geoelectric data through machine learning algorithms for waste leachate detection
No full textElectrical resistivity tomography (ERT) is an effective
method for detecting the leachate plume due to the
plume's very low resistivity values. However, it is
well-known that identifying contaminated areas in landfill
sites based only on the distribution of electrical resistivity
values is highly ambiguous, especially in the presence of
clayey soils, given the low resistivity values that generally
characterize both wet/saturated clays and contamination
plumes. To overcome this problem, the ERT method is
usually combined with the induced polarization method to
derive useful information on leachate detection from the
resistivity, chargeability, and ratio values. In this study,
we developed a tentative methodology for leachate
detection based on clustering analysis of geoelectrical
data. The k-means algorithm was applied to perform a
cluster analysis of the inverted resistivity and chargeability
data acquired in a landfill site in the Campania region
(southern Italy). This site is in a geological context
characterized by silty-clayey deposits, with intercalations
of graded sandstones from the Miocene age. Therefore, it
represents a meaningful test bench for investigations
integrating different geophysical datasets
La terapia dell'ipertensione arteriosa nel controllo del rischio cardiovascolare globale
No full textAnalysis of geoelectric data through machine learning algorithms for waste leachate detection
No full textElectrical resistivity tomography (ERT) is an effective method for detection of the leachate plume due to the very low resistivity values of plumes. However, it is well known that the identification of contaminated areas in landfill sites based only on the distribution of electrical resistivity values is highly ambiguous especially in presence of clayey soils, given the low resistivity values that generally characterize both wet / saturated clays and contamination plumes. To overcome this problem, the ERT method is generally used in combination with the induced polarization method to derive useful information on leachate detection from the values of resistivity, chargeability, and their ratio. In this study, we develop a tentative methodology for leachate detection based on clustering analysis of geoelectrical data. k-means algorithm is applied to perform a cluster analysis of the inverted resistivity and chargeability data acquired in a landfill site located in the Campania region (southern Italy). This site is in a geological context characterized by silty-clayey deposits, with intercalations of graded sandstones from the Miocene age and, therefore, it represents a meaningful test bench for investigations integrating different geophysical datasets
Seismically-induced landslide susceptibility evaluation: Application of a new procedure to the island of Ischia, Campania Region, Southern Italy
No full textIn this paper we present an approach for evaluating landslide susceptibility in seismic areas. It is known that
earthquake-induced landslide susceptibility is related to several, often interplaying, factors. Nevertheless, an
effective grade-2 zonation should be characterized by a good balance between simplicity, quickness and
reliability. The GIS-based procedure we present employs only three factors that we believe are the most
significant in this susceptibility assessment: the type of outcropping rocks/soils, the slope angle and the MCS
intensity. The local annual precipitation, certainly an essential factor, is considered here as a parameter whose
seasonal pattern is constant in time and space. Each of the three parameters is expressed as a Significance
percentage and the resulting Seismic Landslide Susceptibility level of an area is given by the average of the
significance of the first two factors multiplied by the significance of the third factor. The procedure was set and
tested on the volcanic island of Ischia (southern Italy), which was affected by several historical earthquake-induced
landslides. The results of this susceptibility zonation test at Ischia show a very good match between
the distribution of the sources of historical landslides and the areas we identified as the most susceptible ones
k-Means Clustering of geophysical tomographic data for landfill characterization
No full textThe detection and imaging of landfills is a challenging task for geophysical methods because major
pitfalls may arise, in such complex areas, from the speculative interpretation of geophysical
anomalies as geological or antrophic features. In fact, when we face a multi-layered scenario, with
numerous resistive to conductive transitions (that is the case of landfills), the actual shape and
position of the anomalies (e.g. due to leachate accumulation) can be biased. The use of electrical
resistivity tomography (ERT) in combination with the induced-polarization (IP) method, can help in
this sense, even though may be not sufficient to completely remove ambiguities in interpretation
of inverted models.
In this work, we present an application of an unsupervised machine learning k-means algorithm to
ERT and IP data acquired in two urban waste disposal sites. The aim of the cluster analysis is to
reduce the ambiguity on geophysical model interpretation and to improve the accuracy on
detection of anomalous zones related to leachate accumulation. Experimental 2D field data were
firstly inverted separately for resistivity and chargeability, using a Gauss-Newton algorithm. Then,
joint 2D sections were obtained using k-means clustering of electrical resistivity, chargeability and
normalized chargeability (chargeability divided by the resistivity) data. The retrieved model
sections provide a quantitative integration of distinct geophysical data, which can offer new
perspectives for the characterization of leachate distribution in landfills
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
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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