scholarly journals Geostatistical Analysis of Groundwater Data

2019 ◽  
Vol 9 (1) ◽  
pp. 2734-2741
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster ◽  
Geostatistical Analysis ◽  
Water Requirements ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Observation Wells ◽  
Surface And Groundwater

Water resources are stressed because of the country's increasing population and increased water requirements. Even though a good understanding of both surface and groundwater hydrological systems make it possible to manage these resources properly. To study the main characteristics of formation of clusters of groundwater levels, statistical analysis has been used. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. The Statistical analysis is applied to monthly groundwater levels fluctuation data over a period of 2004-2017 in Mysuru, Mandya, Chamarajanagara and Hassan districts of Southern Karnataka in India. The groundwater levels data is collected from 197 Observation Wells from the districts. The Statistical methods like K-Means Clustering and Agglomerative Hierarchical Cluster Analysis is used to perform the datasets. Grouping is made using AHC method, during this process results are obtained by graph called Dendrogram. The obtained results are compared with the LULC maps of all 4 districts. Different grouping (cluster) is made for groundwater level fluctuations for proper conclusion to arrive.

scholarly journals The skipper butterflies (Lepidoptera: Hesperiidae) of the Reserva Ecológica Leonelo Oviedo, San José, Costa Rica

2019 ◽  
Vol 67 (2SUPL) ◽  
pp. S228-S248
Author(s):  
Luis-Ricardo Murillo-Hiller ◽  
Oscar-Antonio Segura-Bermúdez ◽  
Juan-Diego Barquero ◽  
Federico Bolaños
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Costa Rica ◽  
Hierarchical Cluster ◽  
R Package ◽  
Minimum Variance ◽  
Shannon Index ◽  
Wet Season ◽  
Identification Guide ◽  
Different Seasons

Hesperiidae is one of the most diverse families of butterflies in Costa Rica, with approximately 486 species. Even so, there are few butterfly lists where this group has been included. In this paper, we present information on seasonality, abundance and natural history features of this family for the Leonelo Oviedo Ecological Reserve (RELO), a 2 ha forest embedded in an urban matrix. Over the course of two years, a monthly sampling was carried out on a 270 m trail across the Reserve from 08:00 to 12:00, collecting all the individuals located within 5 m on each side of the trail. To better represent the richness, individuals were also randomly collected for more than ten years, but the butterflies collected in this way were not included in the statistical analysis. Photographs were taken of all the species in order to provide an identification guide. For the cryptic species, drawings and dissections of the genitalia were made. For the community indexes we used Microsoft Excel and the Shannon index with base two logarithm. For the summary of the monthly data analysis were done according to dry and wet season. For a comparison of richness and abundance we did a g-test to evaluate if there are differences between seasons; however, with the use of the R package vegan a hierarchical cluster analysis was done using the Jaccard index with Wards minimum variance agglomerative method. With R package pvclust the uncertainty of the clusters based on a bootstrap with 10 000 iterations. 423 individuals of 49 species were included in the statistical analysis, from a total of 435 individuals of 58 species. A tendency to greater richness and abundance of skippers was found during the dry season. Through the cluster analysis, it was possible to determine that in relation to the diversity of skippers, both wet seasons are grouped significantly (P = 0.05). The dry seasons are also grouped significantly (P = 0.05). The reserve has connectivity with other green areas via a stream. During the wet season, plant growth increases connectivity, which could lead to the entry of new individuals of different species that are not permanent residents of RELO and establish small populations, increasing the richness and abundance of species. This added to the variation in the occurrence of some species of butterflies in response to seasonal variations and differences in the availability of resources in different seasons explains the grouping of species between seasons.

Supplemental Material: A sub-centennial-scale optically stimulated luminescence chronostratigraphy and late Holocene flood history from a temperate river confluence

2020 ◽  
Author(s):  
Ben Pears
Keyword(s):  
Statistical Analysis ◽  
Accumulation Rate ◽  
Sediment Accumulation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sediment Accumulation Rate ◽  
Data Sets ◽  
Raw Data ◽  
Summary Sheet ◽  
Agglomerative Hierarchical Cluster Analysis

S1–S16; Figures S1 (sediment accumulation rate modeled by OxCal and Bacon) and S2 (relative moisture values between OSL and LOI analytical methods); Table S1 (OSL procedure from the Rivers Severn-Teme confluence at Powick, UK); and Data Sets S1 (raw data for the modeled calendric dates, sediment accumulation rate, and sedimentological analyses), S2 (raw and log normalized data for ITRAX XRF analysis and key elements Zr, Rb, Fe, Mn, and heavy metals illustrated in Fig. 2), S3 (individual raw data sets for each 5 cm pOSL run alongside a background sediment sample and a summary sheet of all data and replicates), S4 (raw data, log normalized data, and statistical analysis used in the agglomerative hierarchical cluster analysis illustrated in Fig, 2), S5 (calculated log data of sedimentary analyses by 50 yr period and the statistical analysis used in the principal component analysis illustrated in Fig. 3), and S6 (20 yr grouping for the sediment deposition models for the Severn-Teme confluence at Powick, Broadwas, and Buildwas and climatic datasets illustrated in Fig. 4)<br>

Diversity Assessment of Indian Sunnhemp (Crotalaria juncea L.) Accessions for Enhanced Biomass and Fibre Yield using Geographic Information System Approach

2021 ◽  
Author(s):  
R.T. Maruthi ◽  
A. Anil Kumar ◽  
S.B. Choudhary ◽  
H.K. Sharma ◽  
J. Mitra
Keyword(s):  
Cluster Analysis ◽  
Germplasm Collection ◽  
Hierarchical Cluster ◽  
High Biomass ◽  
Lignocellulosic Substrate ◽  
Phenotypic Data ◽  
Diversity Pattern ◽  
Diversity Assessment ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Fibre Yield

Background: Sunnhemp, a rapid growing, high biomass yielding bast fibre crop has a tremendous potentiality in biofuels sector as a lignocellulosic substrate. In order to capitalize the new found area there is a need to identify high biomass and fibre yielding sunnhemp genotypes. The present study provides details of morphological diversity and geographical distribution pattern of Indian sunnhemp accessions. Methods: A total of 42 germplasm accessions collected from ten different states were evaluated for fibre yield and attributing traits in April-June cropping season. Based on phenotypic data agglomerative hierarchical cluster analysis was performed. Geographical coordinates of germplasm collection site were utilized to derive the spatial genetic diversity pattern for green biomass yield and fibre yield.Result: Phenotypic evaluation revealed significant genetic variability among the genotypes for biomass and fibre yield leading to identification of several promising accessions. Cluster analysis and PCA grouped the 42 sunnhemp accessions into three clusters. Cluster II and III are highly divergent harboring contrasting phenotypes. DIVA-GIS approach identified eastern Rajasthan, western Jharkhand and border area between Bihar and Jharkhand as sites of highest sunnhemp diversity. 

scholarly journals Numerals in authorial Turkish-language texts and the stylometric analysis

2021 ◽  
Vol 270 ◽  
pp. 01038
Author(s):  
Andrei Zenkov ◽  
Eugene Zenkov ◽  
Miroslav Zenkov ◽  
Larisa Sazanova
Keyword(s):  
Cluster Analysis ◽  
Discourse Analysis ◽  
Statistical Analysis ◽  
Frequency Distribution ◽  
Computer Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Literary Texts ◽  
Frequency Distributions ◽  
Turkish Language

Two approaches to the statistical analysis of texts are suggested, both based on the study of numerals occurrence in coherent texts. The first approach is related to the study of the frequency distribution of various leading digits of numerals occurring in the text. These frequencies are unequal: the digit 1 is strongly dominating; usually, the incidence of subsequent digits is monotonically decreasing. The frequencies of occurrence of the digit 1, as well as, to a lesser extent, the digits 2 and 3, are usually a characteristic author’s style feature, manifested in all (sufficiently long) texts of any author. This approach is convenient for testing whether a group of texts has common authorship: the latter is dubious if the frequency distributions are sufficiently different. The second approach is the extension of the first one and requires the study of the frequency distribution of numerals themselves (not their leading digits). The approach yields non-trivial information about the author, stylistic and genre peculiarities of the texts and is suited for the advanced discourse analysis. This paper deals with the application of the second approach to the literary texts in Turkish. We have analysed almost the whole corpus of works by are illustrated by examples of computer analysis of the literary texts by O. Pamuk and Y. Kemal – two of Turkey’s most prominent novelists. The hierarchical cluster analysis based on the occurrence of numerals in the texts by Pamuk and Kemal shows the author, genre, and chronology differences of numerals usage in the literary texts of these authors.

scholarly journals Supplemental Material: A sub-centennial-scale optically stimulated luminescence chronostratigraphy and late Holocene flood history from a temperate river confluence

2020 ◽  
Author(s):  
Ben Pears
Keyword(s):  
Statistical Analysis ◽  
Accumulation Rate ◽  
Sediment Accumulation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Sediment Accumulation Rate ◽  
Data Sets ◽  
Raw Data ◽  
Summary Sheet ◽  
Agglomerative Hierarchical Cluster Analysis

S1–S16; Figures S1 (sediment accumulation rate modeled by OxCal and Bacon) and S2 (relative moisture values between OSL and LOI analytical methods); Table S1 (OSL procedure from the Rivers Severn-Teme confluence at Powick, UK); and Data Sets S1 (raw data for the modeled calendric dates, sediment accumulation rate, and sedimentological analyses), S2 (raw and log normalized data for ITRAX XRF analysis and key elements Zr, Rb, Fe, Mn, and heavy metals illustrated in Fig. 2), S3 (individual raw data sets for each 5 cm pOSL run alongside a background sediment sample and a summary sheet of all data and replicates), S4 (raw data, log normalized data, and statistical analysis used in the agglomerative hierarchical cluster analysis illustrated in Fig, 2), S5 (calculated log data of sedimentary analyses by 50 yr period and the statistical analysis used in the principal component analysis illustrated in Fig. 3), and S6 (20 yr grouping for the sediment deposition models for the Severn-Teme confluence at Powick, Broadwas, and Buildwas and climatic datasets illustrated in Fig. 4)<br>

scholarly journals Use of cluster analysis to monitor novel coronavirus-19 infections in Maharashtra, India

2020 ◽  
Vol 72 ◽  
pp. 44-48 ◽  
Author(s):  
Sanjay Kumar
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Healthcare Management ◽  
The State ◽  
Optimum Number ◽  
Box Plots ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Medical Facilities ◽  
Novel Coronavirus ◽  
Almost All

Objectives: A novel coronavirus disease (COVID-19) has been continuously spreading in almost all the districts of the state Maharashtra in India. As a part of the healthcare management development, it is very important to monitor districts affected due to novel coronavirus (COVID-19). The main objective of this study was to identify and classify affected districts into real clusters on the basis of observations of similarities within a cluster and dissimilarities among different clusters so that government policies, decisions, medical facilities (ventilators, testing kits, masks, treatment etc.), etc. could be improved for reducing the number of infected and deceased persons and hence cured cased could be increased. Material and Methods: In the study, we focused on COVID-19 affected districts of the state Maharashtra of India. We applied agglomerative hierarchical cluster analysis, one of data mining techniques to fulfill the objective. Elbow method was used for obtaining an optimum number of clusters for further analysis. The study of variations among various clusters for each of the variables was performed using box plots. Results: Results obtained from the Elbow method suggested three optimum numbers of clusters for each of the variables. For confirmed and cured cases, cluster I corresponded to the districts BI, GO, ND, PA, SI, WS, JN, CH, OS, HI, NB, JG, RT, LA, KO, AM, ST, BU, DH, AK, YTL, SN, AH, SO, AU, RG, NG, NS and PL. Cluster II corresponded to the districts TH and PU and cluster III corresponded to the district MC. For the death cases, cluster I corresponded to the districts BI, GO, ND, PA, SI, WS, JN, CH, OS, HI, NB, JG, RT, LA, KO, AM, ST, BU, DH, AK, YTL, SN, AH, SO, AU, RG, NG, NS, PL and TH. Cluster II corresponded to the district PU and cluster III corresponded to the district MC. Conclusions: The study showed that the district MC under cluster III was affected severely with COVID-19 which had high number of confirmed cases. A good percentage of cured cases were found in some of the districts under cluster I where six districts (GO, SI, CH, OS, SN) had 100% success rate to cure patients. It was observed that the districts TH, PU and MC under clusters II and III had severe conditions which need optimization of medical facilities and monitoring techniques like screening, closedown, curfews, lockdown, evacuations, legal actions, etc.

A corpus-based analysis of the verb pleróo in Ancient Greek

2017 ◽  
Vol 15 (1) ◽  
pp. 253-287 ◽  
Author(s):  
Georgios Ioannou
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster Analysis ◽  
Multiple Correspondence Analysis ◽  
Hierarchical Cluster ◽  
Semantic Features ◽  
Ancient Greek ◽  
Image Schemas ◽  
Inductive Methods ◽  
Image Schema

Abstract This is a corpus-based study of the development of the verb pleróo in Ancient Greek, originally meaning fill, from the 6th c. bce in Classical Greek, up to the end of the 3rd c. bce in Hellenistic Koiné. It implements a hierarchical cluster analysis and a multiple correspondence analysis of the sum of the attested instances of pleróo of that period, divided by century. It explores the gains following a syncretism between two methodological strands: earlier introspective analyses postulating variant construals over intuitively grasped schematic configurations such as image schemas, and strictly inductive methods based on statistical analyses of correlations between co-occurring formal and semantic features. Thus, it examines the relevance of the container image-schema to the architecture of the schematic construction corresponding to the prototypical and historically preceding sense of pleróo, fill. Consequently, it observes how shifts in the featural configurations detected through statistical analysis, leading to the emergence of new senses, correspond to successive shifts on the perspectival salience of elements in the schematic construction of the verb.

Statistical Analysis of Parieto-Frontal Cognitive-Motor Networks

2009 ◽  
Vol 102 (3) ◽  
pp. 1911-1920 ◽  
Author(s):  
Bruno B. Averbeck ◽  
Alexandra Battaglia-Mayer ◽  
Carla Guglielmo ◽  
Roberto Caminiti
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Anatomical Connectivity ◽  
Statistical Structure ◽  
Cortical Areas ◽  
Prefrontal Cortical ◽  
Primate Brain ◽  
Statistical Regularities

Considerable information has been gathered on the anatomical connectivity within the parieto-frontal network of the primate brain. To examine the statistical regularities in this connectivity, we carried out hierarchical cluster analysis and found statistically significant clusters of areas: four in the parietal and six in the frontal lobe. Clusters were based on patterns of inputs from all cortical areas. Both parietal and frontal clusters were composed of sets of spatially contiguous architectonic areas. The four parietal clusters were composed of sets of anterior (somatosensory), dorsal, inferior, and medio-lateral parietal cortical areas. The six frontal clusters were composed of sets of dorsal premotor, ventral premotor, primary motor, cingulate motor, and dorsal and ventral prefrontal cortical areas. Furthermore, connectivity between frontal and parietal clusters was topographic and reciprocal. Thus we found substantial statistical structure and organization in the parieto-frontal network that gives a simplified but accurate description of this system.

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