scholarly journals Multivariate Statistics in Single Observation Space

2022 ◽  
pp. 17-30
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Data Analysis ◽  
Quality Data ◽  
Statistical Techniques ◽  
Observation Data ◽  
Multivariate Statistical Techniques ◽  
Process Data ◽  
Multivariate Statistical ◽  
Single Observation ◽  
Industrial Data ◽  
Observation Space

AbstractThe observation data collected from continuous industrial processes usually have two main categories: process data and quality data, and the corresponding industrial data analysis is mainly for the two types of data based on the multivariate statistical techniques.

scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

scholarly journals Using multivariate statistical techniques to assess water quality of Nhu Y river in Thua Thien Hue province

2014 ◽  
Vol 17 (2) ◽  
pp. 50-60
Author(s):  
Ky Minh Nguyen ◽  
Lam Hoang Nguyen
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

The aims of this research are to assess water quality by organic and nutrient matters and identifying the environmental pressures, examine the impact of the loads to Nhu Y River, Thua Thien-Hue Province. Five stations were sampled at Nhu Y River, the research had monitoring of water quality parameters such as Temperature (Temp), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Nitrate (NO3-) and Phosphate (PO43-). The research used multivariate statistical techniques such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) to assess water quality. The correlation analysis shown a strong positive correlation exists between water quality parameters such as TempDO and BOD5COD (p<0.01). The PCA technique was applied to water quality data sets, which was obtained from Nhu Y River and the results show that the indices which has changed water quality. The results of the PCA using a varimax rotation technique were illustrated with two principal components (PC) and accounts for 62.207% of the overall total variance. The first PC accounted for 40.873% of the total variance, which was loaded with Temp, DO, BOD5 and COD. The second PC consists of NO3- and PO43- which accounts for 21.334% of the total variance, it can be due to the discharge of agricultural activities. Similarly, the CA has identified two major clusters involving: BOD5, COD, Temp, DO (the first cluster) and NO3-, PO43- (the second cluster).

scholarly journals Evaluation of significantly modified water bodies in Vojvodina by using multivariate statistical techniques

2013 ◽  
Vol 67 (5) ◽  
pp. 823-833
Author(s):  
Svetlana Vujovic ◽  
Srdjan Kolakovic ◽  
Milena Becelic-Tomin
Keyword(s):  
Water Quality ◽  
Water Bodies ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Data Sets ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Cluster Properties

This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA)/principal component analysis (PCA) and cluster analysis (CA), were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA) is an objective technique to identify natural groupings in the set of data. CA divides a large number of objects into smaller number of homogenous groups on the basis of their correlation structure. CA combines the data objects together to form the natural groups involving objects with similar cluster properties and separates the objects with different cluster properties. CA showed similarities and dissimilarities among the sampling sites and explain the observed clustering in terms of affected conditions. Using FA/PCA and CA have been identified water bodies that are under the highest pressure. With regard to the factors identified water bodies are: for factor F1 (Plazovic, Bosut, Studva, Zlatica, Stari Begej, Krivaja), for factor F2 (Krivaja, Keres), for factor F3 (Studva, Zlatica, Tamis, Krivaja i Keres) and for factor F4 (Studva, Zlatica, Krivaja, Keres).

scholarly journals Assessment of Tigris River Water Quality Using Multivariate Statistical Techniques

2019 ◽  
Vol 26 (4) ◽  
pp. 26-31
Author(s):  
Muntasir Shareef
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Complex Nature ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Tigris River

The present study uses the multivariate statistical techniques by applying the Factor Analysis (Principle component method) to explain the observed water quality data of Tigris river within Baghdad city. The water quality was analyzed at eleven different sites, along the river, over a period of one year (2017) using 20 water quality parameters. Five factors were identified by factor analysis which was responsible from the 72.291% of the total variance of the water quality in the Tigris river. The first factor called the pollution factor explained 34.387% of the total variance and the second factor called the surface runoff and erosion factor explained 11.875% of the total variance. While, the third, fourth, and fifth factors explained 10.213%, 8.861% and 6.956% of the total variance and called pH, Silica and nutrient factors, respectively. Multivariate statistical techniques can be effective methods to aid water resources managers understand complex nature of water quality issues and determine the priorities to sustain water quality.

Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina

2014 ◽  
Vol 46 (3) ◽  
pp. 377-388 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Ledesma ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multidimensional Data ◽  
Statistical Techniques ◽  
Data Sets ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Data Set

Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003–2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.

Sign in / Sign up

Export Citation Format

Share Document