• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.757-768
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• 2015

Recently, climate change has affected short time concentrated local rainfall and unexpected heavy rain which is increasingly causing life and property damage. In this research, arithmetic average analysis, weighted average analysis, and principal component analysis are used for predicting flood risk. This research is foundation for application of predicting flood risk based on annals of disaster and status of urban planning. Results obtained by arithmetic average analysis, weighted average analysis, and principal component analysis using many factors affect on flood are compared. In case of arithmetic average analysis, each factor has same weights though it is simple method. In case of weighted average analysis, correlation factors are complex by many variables and multicollinearty problem happen though it has different weights. For solving these problems, principal component analysis (PCA) is used because each factor has different weights and the number of variables is smaller than other methods by combining variables. Finally, flood risk assessment considering flood control ability and urban environment risk in former research is predicted.

• Advances in environmental research
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• v.6 no.1
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• pp.53-65
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• 2017

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 15 physico-chemical parameters collected from 7 monitoring stations in a river during the years from 2014 to 2016 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except alkalinity, which is always the most important parameters in contributing to water quality variations for all three seasons.

• Fashion & Textile Research Journal
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• v.12 no.1
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• pp.80-85
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• 2010

The purpose of this study was to examine the characteristics of armscye circumference which will be used to develop total contents for the apparel industry. The subjects of this study were 16- to 49-year-old women whose 3D body shape data were analyzed. 72 length and length-ratio measurements were taken to each subject' armscye circumference. The used analysis methods are descriptive statistics, principal component analysis, and cluster analysis. The results are follows; 1. Considering the Length of armscye circumference, the result of principal component analysis were extracted 3 factors and those factors comprised 95% of total variance. As the result of the cluster analysis of factor scores, subjects were classified into 4 cluster by their size characteristic. 2. Considering the length-ratio of armscye circumference, the result of principal component analysis were extracted 5 factors and those factors comprised 96.45% of total variance. As the result of the cluster analysis of factor scores, subjects were classified into 5 cluster by their shape characteristic. So that, this research could be useful to manufacture garment which reflected 3D body figure and improved fitting.

• Journal of Environmental Health Sciences
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• v.33 no.4
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• pp.299-305
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• 2007

Distribution of fecal pollution indicator bacteria and environmental parameter were investigated of urban artificial lakes. An average concentration of temperature, pH, SS, DO, $COD_{Mn}$, T-P, T-N, Turbidity, Chl-a were $21.5^{\circ}C$, 8.07, 116.70 mg/l, 8.66 mg/l, 2.24 mg/1, 0.52 mg/l, 1.71mg/l, 80.54 NTU, and 52.12 mg/l respectively. From the results of bivariate correlation analysis, fecal contamination indicator bacteria were found to be mutually correlated. And turbidity and suspended solid were correlated. From the results of principal component analysis, four factors were extracted. And four factors of variance explained up to 81.5 percentage. Factor 1 was pollution pattern by fecal contamination, factor 2 was physical pollution pattern by pollution source, factor 3 was natural pollution by precipitation, and factor 4 was artificial pollution pattern by organism.

• Journal of Construction Engineering and Project Management
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• v.5 no.3
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• pp.1-9
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• 2015

Project cost is an important concern in any construction project. Although there has been a lot of studies on factors affecting the cost of construction projects, there seems no consensus as what cost factors have direct influence on the cost of civil engineering projects. This study therefore aims to bridge the current knowledge gap by examining quantity surveyors' perception of the factor structure among nineteen costing attributes identified based on literature review. Questionnaire was used to elicit responses from quantity surveyors working in the Hong Kong construction industry. Principal component analysis is conducted to extract the factor structure of the cost attributes and the attributes are grouped into three factor components, namely the contract management factor, the project management factor and the monetary value factor. Understanding these cost impact factors could be crucial in managing civil engineering projects, since it allows the project stakeholders and quantity surveyors to take precautionary steps to identify the cost management problems and areas for improvement and could even help to avoid cost deviations in engineering projects.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.71-78
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• 2016

The study of water environment system in Changnyeong-Haman weir section using a statistical analysis has been conducted. Statistical analyses used in this study were the correlation analysis, the principal components, and the factor analysis. The purpose of the study is to establish better understanding of relationships between water quality factors in the Changnyeong-Haman weir section which can provide useful information to manage Nakdong river. According to correlation analyses on COD and TOC, it revealed that the value of correlation coefficient was 0.844. Furthermore, the results from the principal component analysis categorized the water quality factors into three factor groups, the first principal factor group included COD, TOC, BOD, pH, water temperature (WT). And, it was observed that the concentration of cyanobacteria in the water body decreased, while the concentrations of the diatoms and the green algae increased after the events of rainfall.

• Korean Journal of Food Science and Technology
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• v.28 no.6
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• pp.1171-1176
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• 1996

The changes in volatiles of the model system were analyzed by GC and GC-MS before and after retorting. The GC data were analyzed statistically by applying the analysis of variance, and 42 peaks were selected at 5% significance level. Multivariate statistical analysis was performed with these 42 peaks as independent variables. Through the stepwise discriminant analysis, 8 peaks, which corresponded to the compounds such as 2-heptanone, cis-3-hexenal, 2-pentyl-furan, 1-methyl-trans-1,2-cyclohexanediol, 2-hexanone, 3-octanone, trans, trans-nona-2,4-dienal and 1-octen-3-ol, were obtained in sequence to distinguish the samples with and without retorting. The principal component analysis of a set of 8 independent variables resulted in 3 principal components which accounted for 96.1% of the variance, while the first principal component (PC 1) explained 76.5% of the total variance. In addition, through the factor analysis of the principal components, the peaks 11, 20 and 21 could be grouped togather in accordance with the direction and the size while the peaks 9, 33 and 39 constituted the second group in the direction.

• 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%.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.3
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• pp.5-15
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• 2015

This research paper has been attempted to inventory the atmospheric factors, contributing to better sales. Exploratory study was undertaken to identify various signs of store atmospherics variables that influence the buying behaviour of buyers. Thirty-four variables identified from this study were used to create a structured questionnaire. This questionnaire was then administered among shoppers in NCR Delhi using non-probability convenience sampling. To determine the atmospheric factors, Principal Component Analysis (PCA) along with Varimax Rotation was attempted. Using principal component factor analysis on the data collected, nine factors were identified to have impact on the store atmospheric. These were Querulous, Music, Sensitive, Budget Seeker, Sensuous, Light, Idler, Space seeker and Comfort Seeker. Contrary to the various earlier studies where music, space seeker and comfort seeker were considered to be most significant factors, light and querulous have emerged out to be the major factor that influences the store atmospheric. This study shows that customers are sensitive, space seekers and sensuous. Constituents of these factors reveal distinct patterns. This research may be used as guidelines for development and management of shopping malls in emerging countries. Retail marketers in India can take this cue in designing their strategies to attract consumers.