scholarly journals Precipitation regionalization of southwest monsoon by hierarchical cluster analysis

MAUSAM ◽  
2021 ◽  
Vol 60 (2) ◽  
pp. 185-196
Author(s):  
A. B. MAZUMDAR
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Monsoon Rainfall ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Southwest Monsoon ◽  
Different Types ◽  
Synoptic Features ◽  
Southwest Monsoon Rainfall ◽  
Homogeneous Regions

An attempt has been made to identify coherent zones of southwest monsoon rainfall over the Indian region by employing hierarchical cluster analysis.  Examination of dendrograms produced by different fusion strategies revealed the presence of 13 nuclei clusters of meteorological subdivisions. Formation of these nuclei clusters could be interpreted by their average principal component (PC) scores and associated synoptic features of PCs.  Higher level inter-nuclei joinings have occurred in various fusion strategies to produce different types of clusters of subdivisions.                 A flexible strategy providing well separated groups of meteorological sub-divisions has been found to be suitable. The method has identified six homogeneous regions of rainfall over India. The meteorological subdivisions have been found to be evenly distributed in these coherent zones. The clustering obtained by this method has been reasonable and largely interpretable.

scholarly journals Southwest monsoon rainfall in India : Part II - Principal components in temporal domain

MAUSAM ◽  
2021 ◽  
Vol 49 (3) ◽  
pp. 301-308
Author(s):  
A. B. MAZUMDAR
Keyword(s):  
Principal Components ◽  
Monsoon Rainfall ◽  
Principal Component ◽  
Southwest Monsoon ◽  
Temporal Domain ◽  
Synoptic Features ◽  
Southwest Monsoon Rainfall ◽  
Relationship Of ◽  
The Relationship ◽  
Low Pressure Systems

An attempt has been made towards objective identification of phases of the southwest monsoon by principal component analysis (PCA) in temporal domain (T-mode). The method utilizes the relationship of weekly rainfall activities with principal components (PCs) of southwest monsoon. Based on the relationships, subgroup of weeks with similar spatial patterns have been identified. Synoptic features of these subgroups have been brought out with the help of synoptic charts. The first four significant PCs are associated with four kinds of active phases of the southwest monsoon when the low pressure systems have typical characteristics corresponding to each PC. Thus, the study suggests a method of interpretation of PCs with the help of synoptic charts by objective identification of phases of southwest monsoon.

scholarly journals Multivariate characteristics of selected grass varieties for seed production

2019 ◽  
Vol 55 (No. 2) ◽  
pp. 83-86
Author(s):  
Marzena Iwańska ◽  
Danuta Martyniak ◽  
Marcin Martyniak ◽  
Dariusz Gozdowski
Keyword(s):  
Principal Component Analysis ◽  
Growth Rate ◽  
Cluster Analysis ◽  
Seed Production ◽  
Plant Resistance ◽  
Hierarchical Cluster Analysis ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Festuca Rubra

Data were obtained in a field experiment carried out at Plant Breeding and Acclimatization Institute Radzikow (central Poland) in 2009–2011. The aim of this study was a multivariate evaluation of 13 advanced lines and cultivars of Festuca rubra, taking into account traits important in seed production. Eleven traits of the grasses and plant resistance to diseases were evaluated. On the basis of multivariate analyses, i.e. hierarchical cluster analysis and principal component analysis, groups of varieties were separated and described, relationships between the traits were evaluated as well. The traits with the biggest influence on multivariate diversity of examined varieties were correlated with the first principal component i.e. height of plants, seeds yield, growth rate of plants, leaf width and time to beginning of earing.  

scholarly journals DAKSHIN BHARTIYA PADHAR PAR HONE WALI MONSOON WARSHA KA PRADHAN AVYAV VISHLESHAN

MAUSAM ◽  
2021 ◽  
Vol 48 (1) ◽  
pp. 77-82
Author(s):  
O.P. SINGH
Keyword(s):  
Principal Components ◽  
Andhra Pradesh ◽  
Monsoon Rainfall ◽  
Principal Component ◽  
Southwest Monsoon ◽  
Northeast Monsoon ◽  
Northeast Monsoon Rainfall ◽  
Total Variability ◽  
Southwest Monsoon Rainfall ◽  
The Impact

 The result of the Principal Component Analysis of southwest and northeast monsoon rainfall on the southern India plateau have been discussed. Monsoon rainfall data of five meteorological sub-divisions, i.e., Coastal Andhra Pradesh, Rayalseema, Tamilnadu, Interior parts of South Karnataka & Kerala, for a period of 33 years (1960-92), have been utilized. The results indicate that the rainfall of Coastal Andhra Pradesh and Rayalseema has maximum impact on first principal component of southwest monsoon rainfall of five meteorological sub-divisions. The study of only first principal component is sufficient in order to understand the 49% of total variability of southwest monsoon rainfall. Analysis of first three principal components is important to understand 85% of total variability of the rainfall of this season.   On the first principal component of northeast monsoon rainfall of aforesaid five meteorological sub-divisions the impact of the rainfall of Kerala and south interior Karnataka has been found maximum. In order to understand the 56% of total variability the analysis of first principal component is sufficient.   The special negative relation is found between northeast monsoon rainfall on the Coastal Andhra Pradesh and southwest monsoon rainfall of previous year on this very sub-division and Rayalseema. The principal components of southwest monsoon rainfall may prove useful for forecasting the northeast monsoon rainfall of southern Indian plateau.  

scholarly journals Polymorphism analysis in some Algerian Opuntia species using morphological and phenological UPOV descriptors

2017 ◽  
Vol 95 (3) ◽  
pp. 391 ◽  
Author(s):  
Boubakr Hadjkouider ◽  
Ammar Boutekrabt ◽  
Bahia Lallouche ◽  
Salim Lamine ◽  
Néjia Zoghlami
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Phenotypic Diversity ◽  
Principal Component ◽  
Morphological Diversity ◽  
Hierarchical Cluster ◽  
Relative Magnitude ◽  
Polymorphism Analysis ◽  
Opuntia Ficus Indica ◽  
New Varieties

<p><strong>Background: </strong>In the present study, we have investigated the morphological variation in a set of five <em>Opuntia</em> species from the Algerian steppes using 49 UPOV descriptors.</p><p><strong>Questions: </strong>which of the 49 descriptors that can be used as powerful estimators of the phenotypic diversity within <em>Opuntia</em> species? How is the morphological diversity patterned in Algerian <em>Opuntia</em>?</p><p><strong>Species study/ Mathematical model: </strong><em>Opuntia ficus-indica, Opuntia amycleae, Opuntia streptacantha, Opuntia engelmannii, Opuntia robusta</em><strong>.</strong> Principal Components Analysis (PCA) and Hierarchical Cluster Analysis were used.</p><p><strong>Study site: </strong>Four counties were studied located in the Algerian steppes. The present research was carried out during 2014.</p><p><strong>Methods:</strong> 49 descriptors adopted by the International Union for the Protection of New Varieties of Plants (UPOV) were employed in the present research, where cladode, flower and fruit traits were used to determine the overall degree of polymorphism among 5 <em>Opuntia</em> species.</p><p><strong>Results:</strong> Principal Component Analysis and Hierarchical Cluster Analysis indicated a consistent differentiation between all studied species. The relative magnitude of the first two PCA eigenvectors showed that 8 descriptors out of 49 were identified as the most important descriptors for the classification of the species. The dendrogram performed on the calculated Euclidean distances between all species pairs allowed the identification of 3 groups, unlike the PCA that identified 4 groups. The species <em>Opuntia ficus-indica </em>and <em>Opuntia amycleae</em> were identified as very close morphologically.</p><p><strong>Conclusions: </strong>The present outcome represents a paramount step towards the fast selection of interesting species and for their best management and conservation.</p>

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

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