scholarly journals Multivariate statistical analysis to support the minimum streamflow regionalization

2015 ◽  
Vol 35 (5) ◽  
pp. 838-851 ◽  
Author(s):  
Abrahão A. A. Elesbon ◽  
Demetrius D. da Silva ◽  
Gilberto C. Sediyama ◽  
Hugo A. S Guedes ◽  
Carlos A. A. S. Ribeiro ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
River Basin ◽  
Multivariate Statistical Analysis ◽  
Annual Rainfall ◽  
Morphometric Characteristics ◽  
Multivariate Statistical ◽  
Independent Variables ◽  
Homogeneous Regions ◽  
Doce River

ABSTRACT This study aimed to develop a methodology based on multivariate statistical analysis of principal components and cluster analysis, in order to identify the most representative variables in studies of minimum streamflow regionalization, and to optimize the identification of the hydrologically homogeneous regions for the Doce river basin. Ten variables were used, referring to the river basin climatic and morphometric characteristics. These variables were individualized for each of the 61 gauging stations. Three dependent variables that are indicative of minimum streamflow (Q7,10, Q90 and Q95). And seven independent variables that concern to climatic and morphometric characteristics of the basin (total annual rainfall – Pa; total semiannual rainfall of the dry and of the rainy season – Pss and Psc; watershed drainage area – Ad; length of the main river – Lp; total length of the rivers – Lt; and average watershed slope – SL). The results of the principal component analysis pointed out that the variable SL was the least representative for the study, and so it was discarded. The most representative independent variables were Ad and Psc. The best divisions of hydrologically homogeneous regions for the three studied flow characteristics were obtained using the Mahalanobis similarity matrix and the complete linkage clustering method. The cluster analysis enabled the identification of four hydrologically homogeneous regions in the Doce river basin.

Quality Evaluation of Canned Whole Tomatoes

1995 ◽  
Vol 1 (2-3) ◽  
pp. 97-104 ◽  
Author(s):  
S. Porretta
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quality Evaluation ◽  
Chemical Properties ◽  
Sensory Attributes ◽  
Tomato Juice ◽  
Multivariate Statistical ◽  
Tomato Variety ◽  
Physico Chemical

The physico-chemical properties of commercial canned whole tomatoes (i.e., peeled tomatoes with about 30% tomato juice as packing medium) and the contribution of various analytical parameters to some sensory attributes were evaluated using multivariate statistical analysis. In addition, cluster analysis was used to determine the existence of significant qualitative differences between the old and famous San Marzano variety (as described on the commercial labels by the manufacturers) and traditional (without any specification on the tomato variety) canned whole tomatoes.

scholarly journals Application of multivariate statistical analysis in the assessment of groundwater quality of Tan Thanh district, Ba Ria – Vung Tau province

2017 ◽  
Vol 1 (M2) ◽  
pp. 66-72
Author(s):  
Au Hai Nguyen ◽  
Ngan Thi Khanh Phan ◽  
Thuy Thi Thanh Hoang ◽  
Ngoc Nguyen Hong Phan
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Anthropogenic Pollution ◽  
Dry Season ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
And Cluster Analysis

In the present study, Multivariate Statistical Analysis (MSA) such as Principle Component Analysis (PCA) and Cluster Analysis (CA) were applied to determine the temporal and spatial variations of groundwater quality in Tan Thanh district, Ba Ria – Vung Tau province. Groundwater samples were collected from 18 monitoring wells in April (dry season) and October (wet season) during the year 2012. Fifteen parameters (pH, TH, TDS, Cl-, F-, NO3-, SO42-, Cr6+, Cu2+, Ca2+, Mg2+, Na+, K+, HCO3- and Fe2+) were selected for MSA. PCA identified a reduced number of mean three latent factors of groundwater quality. Three factors called salinization, water-rock interaction and anthropogenic pollution explanined 70,5% (dry season) and 71.28% (wet season) of the variances. Cluster analysis revealed two main different groups of similarities between the sampling sites. This study presents the necessity of MSA in order to extract more precise information from a huge minitoring data, which will be usefull to groundwater quality management.

Source identification of groundwater pollution with the aid of multivariate statistical analysis

2005 ◽  
Vol 5 (6) ◽  
pp. 281-288 ◽  
Author(s):  
T.N. Wu ◽  
Y.C. Huang ◽  
M.S. Lee ◽  
C.M. Kao
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Ammonia Concentration ◽  
Mineral Dissolution ◽  
Agricultural Activity ◽  
Multivariate Statistical ◽  
The Impact

With the aid of multivariate statistical analysis, this study attempted to predict possible underlying processes, attribute their influence, and isolate the distribution of sources that might threaten groundwater quality. Tainan County, Taiwan was employed as a case study, and 34 monitoring wells were sampled for routine lab analysis. Lab data of groundwater quality including pH, EC, hardness, chloride, sulfate, ammonia, nitrate, Fe, Mn, As, Zn, TOC and TDS were subjected to factor and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, whereas cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples. By utilizing PCA, the identified four major principal components (PCs) representing 78.8% of cumulative variance were able to interpret the most information contained in the data. PC 1 reflects the dominance of salinization, which was characterized by the elevated concentrations of EC, hardness, chloride and sulfate in groundwater. PC 2 with the positive loadings of TOC and pH but negative loading of nitrate is thought to be representative of organic pollution within the aquifer. PC 3 is regarded as mineralization factor on the basis of the loadings of manganese and zinc. PC 4 shows a strong monotonic relationship with ammonia concentration in the groundwater revealing the linkage with agricultural activity. CA results illustrated that coastal area was partially salinized as a result of seawater intrusion and part of salinization zone was also subjected to the impact of mineral dissolution.

scholarly journals Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

2019 ◽  
Vol 11 (12) ◽  
pp. 3345 ◽  
Author(s):  
Guowei Liu ◽  
Fengshan Ma ◽  
Gang Liu ◽  
Haijun Zhao ◽  
Jie Guo ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Water Inrush ◽  
Principal Component ◽  
Component Analysis ◽  
Gold Mine ◽  
Mining Operations ◽  
Multivariate Statistical

Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

scholarly journals Assessment of Surface Water Quality by Using Multivariate Statistical Analysis Techniques: A Case Study of Nhue River, Vietnam

2020 ◽  
Vol 11 (10) ◽  
pp. 488-492
Author(s):  
Nguyen Huu Hue ◽  
◽  
Nguyen Huu Thanh
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Statistical Analysis ◽  
Surface Water ◽  
Spatial Variability ◽  
Multivariate Statistical Analysis ◽  
Surface Water Quality ◽  
Monitoring Site ◽  
Multivariate Statistical ◽  
Analysis Techniques

The aim of this study is to assess the spatial variability and to determine the main contamination sources in surface water quality of the Nhue River, Viet Nam by using multivariate statistical analysis techniques, including principal component analysis (PCA) and cluster analysis (CA). Eight water quality parameters were measured at 21 sites along the Nhue River and its tributaries during irrigated periods from 2016 to 2019. The spatial variability of water quality in the Nhue River and its tributaries was determined separately from cluster analysis. The result determined two tributaries, including Yen Xa Canal (NT9 monitoring site) and To Lich River (NT3 monitoring site) leading to severe pollution at To Bridge (N4 monitoring site) region in the Nhue River. The PCA determined a reduced number of two principal components that explained 47.75% of the total variation in the data. The first PC indicated that water temperature (WT) and pH are the dominant polluting factors which are attributed to craft villages, domestic sewage and industrial wastewater. Following is nitrate nitrogen NO3¯ in the second PC which is related to fertilizer application in the farms nearby. The results indicated that CA multivariate statistical analysis technique is useful for the assessment of the spatial water quality variability in a river which has a number of tributaries.

Sign in / Sign up

Export Citation Format

Share Document