The contribution of cluster and discriminant analysis to the classification of complex aquifer systems

Panagopoulos, George; Angelopoulou, D.; Tzirtzilakis, E. E.; Giannoulopoulos, P.

doi:10.1007/s10661-016-5590-y

Cited by 21 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kriging interpolation is applied to give the good variable nearness value of an approximate in association to the real value employing least square approach [14,15]. Multivariate techniques have been used to find the information from the large data and such techniques applied in exploratory data assessment as approaches to categorize samples and recognize pollution sources [16,17]. Cluster analysis and principal component analysis techniques helps in interpretation of enormous parameters to accomplish a great understanding of the hydrochemical mechanisms engaged [18,19].…”

Section: Introductionmentioning

confidence: 99%

Groundwater quality evaluation of Shiraz City, Iran using multivariate and geostatistical techniques

Alamdar¹,

Kumar

Moghtaderi³

et al. 2019

SN Appl. Sci.

View full text Add to dashboard Cite

Groundwater is vital water resource for domestic, agricultural and industrial purposes in Shiraz City, Iran. Management of groundwater with respect to quality and quantity is decisive subject in order to accomplish the increasing requirements for water. In the present paper, evaluation of groundwater parameters, viz., pH, electrical conductivity (EC), major cations (K + , Na + , Ca 2+ and Mg 2+), major anions (Cl − and HCO 3 −), NO 3 − , SO 4 2− , total dissolved solids (TDS), alkalinity, hardness, sodium absorption ratio (SAR) and Na% were done from Shiraz City, Iran. Consequently, 80 groundwater samples were collected from different sites in June 2017. The results of different parameters were compared with the standard guideline prescribed by the World Health Organization (WHO) for drinking and public health purposes. From the results, it was concluded that 62.5% samples exceeded for EC values, 12.5% samples for Na + , 30% samples for Ca 2+ , 97.5% samples for Mg 2+ and TDS, 28.7% samples for Cl − , and 40% samples for SO 4 2− contents as recommended by WHO. The Na% values ranged from 5.25 to 38.59, whereas SAR values varied from 0.21 to 6.64 meq/L. Results of Na% and SAR indicated that groundwater is good for irrigation and domestic purposes. The results of MGI and BEI showed that 83.75% sampling sites showed shallow meteoric percolation type and Na + SO 4 type with less than one value respectively. Multivariate statistical analysis (cluster analysis and principal component analysis) and spatial maps showed that both anthropogenic activities such as cement factory, gas power plant, Rishmark factory and vegetable Oil Company, and natural processes like rock weathering are responsible for the contents of groundwater parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

Groundwater quality evaluation of Shiraz City, Iran using multivariate and geostatistical techniques

Alamdar¹,

Kumar

Moghtaderi³

et al. 2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…The R-type cluster analysis classifies variables, and the Qtype cluster analysis classifies samples [34]. If you are interested in the mathematical basis of hierarchical clustering analysis, you can find it in the literature [35,36]. In this paper, the Wald method was used to perform Q-type clustering analysis on the original water samples, and the square Euclidean distance was used as the metric to determine the relationships between them by using the statistical software IBM SPSS Statistics 26.…”

Section: Hierarchical Clustering Analysis Hierarchical Clusteringmentioning

confidence: 99%

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Zhai

et al. 2021

Geofluids

View full text Add to dashboard Cite

Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.

show abstract

“…However, the targeted variable in variogram needs to be selected carefully in order to get representative result. The multivariate analysis techniques, such as PCA and DA, can be performed to identify the main variable that describe the difference of multivariables characteristics in the observation area (Panagopoulos et al, 2016).…”

Section: Figure 2 Graph Of Principal Component Analysis (A) and Canomentioning

confidence: 99%

Sampling Design for Water Quality Monitoring in Marine Reserve: A Study Case at Banda Sea Conservation Park

Januar

2018

jpsl

View full text Add to dashboard Cite

Design of sampling site water quality monitoring at marine reserve is critical, to optimize the effectiveness of periodic assessment. A simple stratified systematic design, that been usually used in most of monitoring analysis, may not maximize the information of spatial data in marine hydrology. The present work applied a multivariate statistical analysis and spatial autocorrelation methods to develop an optimal sampling design for water quality assessment in tropical marine reserve, Banda Sea Conservation Park, Indonesia. Seasonal (west, intermediate, and east monsoon season in Indonesia) and spatial (38 stations) water quality analysis (salinity, dissolved oxygen, pH, and nutrient) in 3 zones of Marine Reserve were conducted. Principal Component Analysis (PCA) showed dissolved oxygen (DO) was the principal variable for the sampling design criterion. Spatial DO Variograms suggested elocation of the sampling stations, to optimize the design of water quality monitoring. Therefore, even the principal variable may vary at other locations, depends on hydrology and other area specific characteristics, the proposed technique could be applied in sampling design concerning water quality monitoring.

show abstract

The contribution of cluster and discriminant analysis to the classification of complex aquifer systems

Cited by 21 publications

References 33 publications

Groundwater quality evaluation of Shiraz City, Iran using multivariate and geostatistical techniques

Groundwater quality evaluation of Shiraz City, Iran using multivariate and geostatistical techniques

Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Sampling Design for Water Quality Monitoring in Marine Reserve: A Study Case at Banda Sea Conservation Park

Contact Info

Product

Resources

About