scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

scholarly journals Semi-automated regional classification of the style of activity of slow rock slope deformations using PS InSAR and SqueeSAR velocity data

2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Supervised Machine Learning ◽  
Displacement Rate ◽  
Persistent Scatterer Interferometry ◽  
Internal Damage ◽  
Machine Learning Classification ◽  
Mountainous Regions ◽  
Definition Of

<p>Large slow rock-slope deformations are widespread in alpine environments and mountainous regions worldwide. They evolve over long time by progressive failure processes, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of the activity of these phenomena is thus required to cope with their long-term threats.</p><p>Displacement rates measured by remote sensing and ground-based techniques only provide a snapshot of long-term, variable trends of activity and are insufficient to capture the behavior of slow rock slope deformations in a long-term risk management perspective. We thus propose to adopt a more complete approach based on a re-definition of “style of activity”, including displacement rate, segmentation/heterogeneity, kinematics, internal damage and accumulated strain. To this aim, we developed a novel approach combining persistent-scatterer interferometry (PSI) and systematic geomorphological mapping, to obtain an objective semi-automated characterization and classification of 208 slow rock slope deformations in Lombardia (Italian Central Alps). Through a peak analysis of displacement rate distributions we characterized the degree of internal segmentation of mapped slow rock slope deformations and highlighted the presence of nested sectors with differential activity. Then, we used an original approach to automatically characterize the kinematics of each landslide (translational, compound, or rotational) by combining a 2DInSAR velocity vector decomposition and a supervised machine learning classification. Finally, we combined Principal Component and K-medoid Cluster multivariate statistical analyses to classify slow rock slope deformations into groups with consistent styles of activity. We classified DSGSDs and large landslides respectively in five and two representative groups described by different degree of internal segmentation and kinematics that significant influence the evolutionary behavior and affect the definition of representative displacement rates. Our results provide a statistical evidence that phenomena classified as “Deep-Seated Gravitational Slope deformations” (DSGSD) and “large landslides” actually have different mechanisms and/or evolutionary stages, mirrored by different morphological features that testify higher accumulated internal deformation for large landslides with respect to DSGSDs. Our statistical classification of rock-slope deformation style of activity further highlighted the different risk potentials associated to each one of the seven descriptive groups in a practical perspective, taking into account the most significant parameters (rate, volume and heterogeneity) to assess risks related to the interaction between slow movements and sensitive elements.</p><p>Our analysis benefits from both deterministic and statistical components to perform a complete regional screening of slow rock slope deformations and to prioritize site-specific, engineering geological analyses of critical slopes depending on the most important factors conditioning their long-term style of activity. Our methodology is readily applicable to different datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.</p>

scholarly journals Contribution to the Chromatic Characterization of Unifloral Honeys from Galicia (NW Spain)

Foods ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 233 ◽  
Author(s):  
Olga Escuredo ◽  
María Shantal Rodríguez-Flores ◽  
Sergio Rojo-Martínez ◽  
María Carmen Seijo
Keyword(s):  
Physicochemical Properties ◽  
Principal Component ◽  
Total Content ◽  
Physicochemical Characteristics ◽  
Dependent Variables ◽  
Close Relationship ◽  
Forward Stepwise ◽  
Unifloral Honey

Honey color and other physicochemical characteristics depend mainly on the botanical and geographical origin. The study of these properties could make easier a correct classification of unifloral honey. This work determined the palynological characteristics and some physicochemical properties such as pH, electrical conductivity, and color (Pfund scale and the CIELa*b* coordinates), as well as the total content of the bioactive compounds phenols and flavonoids of ninety-three honey samples. Samples were classified as chestnut, blackberry, heather, eucalyptus, and honeydew honey. The study showed a close relationship between the physicochemical variables and the botanical origin. The five types of honey presented different physicochemical properties among them. A principal component analysis showed that Hue, lightness, b*, and Chroma variables were important for the honey types classification, followed by Erica pollen, pH, Cytisus, and Castanea variables. A forward stepwise regression analysis was performed introducing as dependent variables the color (mm Pfund) and the Chroma and the Hue variables. The regression models obtained explained 86%, 74%, and 86% of the variance of the data, respectively. The combination of the chromatic and physicochemical and pollen variables through the use of multivariable methods was optimal to characterize and group the honey samples studied.

Characterization of olive germplasm by chemical oil components and morphological descriptors in Basilicata region (Italy)

2012 ◽  
Vol 10 (2) ◽  
pp. 145-151 ◽  
Author(s):  
V. Alba ◽  
V. Bisignano ◽  
A. Rotundo ◽  
G. B. Polignano ◽  
E. Alba
Keyword(s):  
A Priori ◽  
Principal Component ◽  
Oil Composition ◽  
Cultivar Group ◽  
Olive Cultivars ◽  
Superior Genotypes ◽  
Coefficients Of Variations ◽  
Oil Components

In this paper, we describe variations among autochthonous olive cultivars from five different areas in Basilicata (Southern Italy) classified according to 33 chemical oil components and morphological traits. While all examined descriptors show no significant differences among cultivars, means and coefficients of variations have been highlighted. Principal component analysis has then been used to reduce the number of descriptors. Cultivars have been classified by cluster analysis into three groups. Following a discussion of cultivar group similarities, results suggest that an ‘a priori’ classification of cultivars according to growing area does not strictly correspond to phenotypic grouping. From the spatial distribution of cultivars, however, it has been possible to identify ‘superior’ genotypes in terms of olive oil composition.

scholarly journals Classification of thermal waters based on their inorganic fingerprint and hydrogeothermal modelling

2011 ◽  
Vol 8 (3) ◽  
pp. 4559-4581 ◽  
Author(s):  
I. Delgado-Outeiriño ◽  
P. Araujo-Nespereira ◽  
J. A. Cid-Fernández ◽  
J. C. Mejuto ◽  
E. Martínez-Carballo ◽  
...  
Keyword(s):  
Surface Waters ◽  
Thermal Water ◽  
Principal Component ◽  
Thermal Waters ◽  
Rock Interaction ◽  
Hydrogeochemical Modelling ◽  
Ancient Times ◽  
Water Rock Interaction

Abstract. Hydrothermal features in Galicia have been used since ancient times for therapeutic purposes. A characterization of these thermal waters was carried out in order to understand their behaviour based on inorganic pattern and water-rock interaction mechanisms. In this way 15 thermal water samples were collected in the same hydrographical system. The results of the hydrogeochemistry analysis showed one main water family of bicarbonate type sodium waters, typical in the post-orogenic basins of Galicia. Principal component analysis (PCA) and partial lest squared (PLS) clustered the selected thermal waters in two groups, regarding to their chemical composition. This classification agreed with the results obtained by the use of geothermometers and the hydrogeochemical modelling. The first included thermal samples that could be in contact with surface waters and therefore, their residence time in the reservoir and their water-rock interaction would be less important than for the thermal waters of the second group.

scholarly journals Soil spectral library and its use in soil classification

2010 ◽  
Vol 34 (3) ◽  
pp. 861-870 ◽  
Author(s):  
Henrique Bellinaso ◽  
José Alexandre Melo Demattê ◽  
Suzana Araújo Romeiro
Keyword(s):  
Spectral Data ◽  
Principal Component ◽  
Soil Classification ◽  
Convex Curve ◽  
Spectral Library ◽  
Spectral Curves ◽  
Sensing Applications ◽  
Agricultural Regions

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

scholarly journals Psychometric properties of the Impulsive/Premeditated Aggression Scale in Portuguese community and forensic samples

2019 ◽  
Vol 41 (2) ◽  
pp. 144-148 ◽  
Author(s):  
Ana Rita Cruz ◽  
Rita Pasion ◽  
Andreia Castro Rodrigues ◽  
Carmen Zabala ◽  
Jorge Ricarte ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Aggressive Behavior ◽  
Psychometric Properties ◽  
Community Sample ◽  
Principal Component ◽  
Clinical Settings ◽  
European Portuguese ◽  
Forensic Samples

Abstract Introduction Aggression can be defined according to impulsive or premeditated features. Impulsivity is defined as an uncontrolled and unplanned form of aggression. On the contrary, premeditation requires planning and is goal-oriented. Objective The purpose of this study was to validate the basic psychometric properties of the Impulsive/Premeditated Aggression Scale (IPAS) into European Portuguese. The scale evaluates aggression according to impulsive and premeditated features, which are considered the predominant forms of aggressive behavior, and can be used in community, forensic and clinical settings. Methods Participants from a community sample (n = 957; 424 male) and incarcerated individuals (n = 115, all male) completed the IPAS. Results Internal consistency and reliability indicated that the scale has good psychometric properties in both samples. Data from a principal component analysis (PCA) demonstrated similarities to previous structures reported in the literature. Conclusions The scale demonstrated to be sensitive to the bimodal classification of aggression in community and forensic samples, indicating its utility in the characterization of aggressive patterns.

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

2018 ◽  
Vol 72 (12) ◽  
pp. 1774-1780 ◽  
Author(s):  
Irene Marivel Nolasco Perez ◽  
Amanda Teixeira Badaró ◽  
Sylvio Barbon ◽  
Ana Paula AC Barbon ◽  
Marise Aparecida Rodrigues Pollonio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Processing Industry ◽  
Chemical Attributes ◽  
Physical And Chemical

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical–chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900–1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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