• Journal of Korean Society on Water Environment
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• v.23 no.6
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• pp.814-821
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• 2007

This study was estimated water quality to raw water quality management of the Maeri intake station in the Nakdong River using Multivariate Analysis. The results of Principle Component Analysis was explained up to 76.9% of total water quality by three principle components. The 1st, 2nd was explained 44.7%, 17.9% and third was explained 14.3%. Also, the three factors was derived from Factor Analysis. The 1st factor was estimated as the matabolism and organic matter pattern related to algal growth. The 2nd factor was judged as the pollution of pattern related to the discharge from stream of the Nakdong River and 3rd factor was viewed as the hydrological variation pattern related to particle matter. The results of Cluster Analysis were classified into three groups.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.297-306
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• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Environmental Engineering Research
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• v.19 no.3
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• pp.197-203
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• 2014

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

• Journal of Environmental Science International
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• v.15 no.11
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• pp.1017-1025
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• 2006

This study was conducted to evaluate characteristic of water duality in coastal sea of Incheon using a multivariate analysis. The analysis data in coastal sea of Incheon was aquired by the NFRDI data which was surveyed from March 1997 to November 2003. Eleven water quality parameters were determined on each survey The results were summarized as follow : Water quality in Incheon coastal sea could be explained up to 64.62% by three factors which were included in loading of fresh water and nutrients by the land(36.98%), seasonal variation(16.19%), and internal metabolism (11.24%). The results of time series analysis by factor score, in case of factor 1, station 1 influenced by Han river was shown to high factor score and station 3 located by outer sea was shown to low factor score. In case of factor 2, station 1 was appeared to high variation and station 3 was appeared to low variation. The result of cluster analysis by station was classified into three group that has different water quality characteristics. Especially, station 1 which affected by Han river and station 4 which affected by sewage treatment plant was appeared to considerable water quality characteristics against other station. In yearly cluster analysis, three group was classified and water quality in 2003 years due to high precipitation was different to another year. It could be suggested from these results that it is important to control discharge of fresh water by Han rivet and sewage treatment plant for water quality management of coastal sea of Incheon.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.117-120
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• 1996

The objective of this study is to suggest modified Covariance Structure Analysis that combine with existing Multivariate Statistical Method which is used Casual Analysis Method in Management Information. For this purpose, we'll consider special feature and limitation about Correlation Analysis, Regression Analysis, Path Analysis and connect Covariance Structure Analysis with Statistical Factor Analysis so that theoretical casual model compare with variables structure in collecting data. A example is also presented to show the practical applicability of this approach.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.6
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• pp.625-632
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• 2015

The study of water environment system using a multivariate analysis in Changnyeong-Haman weir section has been conducted. The purpose of this study is to establish better understanding related water qualities in the Changnyeong-Haman weir section which can provide useful information. The data were consisted of water quality data and algae data including WT(water temperature), pH, DO, EC, COD, SS, T-N, $NH_3-N$, T-P, $PO_4-P$, Chl-a, TOC, d-silica, t-silica, Cyanobacteria, Diatoms, and Green algae. Statistical analyses used in this study were correlation analysis, principal components, and factor analysis. According to correlation analysis on COD and TOC, it revealed that the each value of correlation coefficient was 0.843. On the other result, a negative correlation was observed between diatoms and d-silica. Furthermore, the results of principal component analysis to the overall water quality were classified into four main factors with contribution rate 81.071%.

• Journal of Environmental Science International
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• v.7 no.5
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• pp.591-598
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• 1998

This study was conducted to evaluate water quality in the Keum River estuary using principal component analysis. The results was summarized as follow; Water quality in the Keum River estuary could be explained up to 70.40% by three factors which were included in the inffluent loading by the Keum River and Kyungpo cheon(38.99%), seasonal variation and organic matter pollution(19.05%), sediment resuspension and internal metabolism(12.35%). For spatial variation of factor score, artificial pollutant loading is highest at st.1, below Keum River barrage, and decreases toward the outer sea. For annual variation of factor score, factor 1 was highly related to artificial pollutant leading, and it was gently increased in 1994. Also, organic matter pollution, sediment resuspension and internal metabolism were increased to every year. It is necessary to control the nutrient leading by Keum river and Kyongpo cheon for Water quality management of estuary.

• Journal of Environmental Science International
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• v.13 no.3
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• pp.233-244
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• 2004

This study was conducted to evaluate water quality in the Mankyung River using multivariate analysis. The analysis data which was surveyed from January 1996 to December 2002 in Mankyung river was aquired by the ministry of environment. Twelve water quality parameters were determined on each survey. The results were summarized as follow; Water quality in the Mankyung River could be explained up to 74.90% by four factors which were included in loading of organic matter and nutrients by the tributaries(43.28%), seasonal variation(10.40%), loading of pathogenic bacteria by domestic sewage of Gapcheon (12.41%) and internal metabolism in river(8.81%). The result of cluster analysis by station was classified into three group that has different water quality characteristics. Especially, Iksan river was appeared to considerable water quality characteristics against other station. In monthly cluster analysis, three group was classified by seasonal characteristics. Also, in yearly cluster analysis, three group was classified. It is necessary to control the pollutant loadings by domestic sewage and livestock waste for water quality management of Mankyung river.

• Journal of Environmental Science International
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• v.11 no.12
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• pp.1281-1289
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• 2002

This study was conducted to evaluate water quality in the Keum River using multivariate analysis. The analysis data in Keum river made use of surveyed data by the ministry of environment from January 1994 to December 2001. Thirteen water quality parameter were determined on each sample. The results was summarized as follow; Water quality in the Keum River could be explained up to 71.39% by four factors which were included in loading of organic matter and nutrients by the tributaries (32.88%), seasonal variation (16.09%), loading of pathogenic bacteria by domestic sewage of Gapcheon (13.39%) and internal metabolism in estuary as lakes(9.03%). For spatial variation of factor score, four group was classified by each factor characterization. Station 1 and 2 was influenced by Daechung dam, station 3 was affected by domestic sewage of Gapcheon, station 10~12 was affected by estuary dyke and the rest station. The result of cluster analysis by station was classified into four group that has different water quality characteristics. In monthly cluster analysis, three group was classified according to seasonal characteristic. Also, in yearly cluster analysis, three group was classified. It is necessary to control the pollutant loadings by Gapcheon inflow domestic sewage in Daejeon city for the sake of water quality management of Keum river.