• 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.

• Journal of Environmental Health Sciences
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• v.18 no.2
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• pp.67-74
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• 1992

The lab-scale biological A$_{2}$O system was applied from treating piggery wastewater highly polluted organic material which nitrogen and phosphorous are much contained relatively in conversion with other wastewater. The objective of this study was to investigate the effect of variance parameters on the treatability of this system according to operation conditions. An obtained experimental data were analysed by using principal component analysis (PCA) method. The results are summarized as follows: 1. From Varimax rotated factor loading in raw wastewater, variance of factor 1 was 36.8% and cumulative percentage of variance from factor 1 to factor 4 was 81.5% and of these was related to BOD, TKN and BOD loading. 2. In anaerobic process, variance of factor 1 was 33.5% and cumulative percentage of variance from factor I to factor 4 was 81.8% and of these was related to PO$_{4}$-P, BOD, DO and Temperature. 3. In anoxic process, variance of factor 1 was 30.1% and cumulative percentage of variance from factor i to factor 4 was 84.3% and of these was related to pH, DO, TKN and temperature. 4. In aerobic process, variance of factor 1 was 43.8% and cumulative percentage of variance from factor 1 to factor 4 was 81.5% and of these was highly related to DO, PO$_{4}$-P and BOD. 5. It was better to be operated below 0.30 kg/kg$\cdot$day F/M ratio to keep over 90% of BOD and SS, 80% of TKN, and 60% of PO$_{4}$-P in treatment efficiencies. 6. Treatment efficiencies was over 93% of BOD and SS, 81% of TKN and 60% of PO$_{4}$-P at over 20$^{\circ}$C, respectively.

• 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.

• 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.

• Journal of agriculture & life science
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• v.46 no.2
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• pp.1-8
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• 2012

This study is a precedent study for deriving transfer function model between growing stock and forest management policies. Its goal is to solve the multicollinearity between forest works inducing growing stock changes through principal component analysis using annual time series data from 1997 to 2008. As the results, the total explanatory power showed 91.4% on the summarized 3 principal components. They were renamed 'good forest management' 'pest & insets management' 'forest fires' for conceptualization on the derived each component.

• Environmental Engineering Research
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• v.22 no.1
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• pp.31-39
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• 2017

The primary purpose of this research is to understand those elements that define heavy metals contamination and to propose a novel assessment method based on principal component analysis (PCA) in the Yangping River region of Lingbao City, China. This paper makes detailed calculations regarding such factors the single-factor assessment ($P_i$) and Nemerow's multi-factor index ($P_N$) of heavy metals found in the surface water of the Yangping River. The maximum values of $P_i$ (Cd) and $P_i$ (Pb) were determined to be 892.000 and 113.800 respectively. The maximum value of $P_N$ was calculated to be 639.836. The results of Pearson's correlation analysis, hierarchical cluster analysis, and PCA indicated heavy metal groupings as follows: Cu, Pb, Zn and As, Hg, Cd. The PCA-based pollution index ($P_{an}$) of samplings was subsequently calculated. The relative coefficient square was valued at 0.996 between $P_{an}$ and $P_N$, which indicated that $P_{an}$ is able to serve as a new heavy metal pollution index; not only this index able to eliminate the influence of the maximum value of $P_i$, but further, this index contains the principal component elements needed to evaluate heavy metal pollution levels.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.2
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• pp.12-23
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• 1994

In order to investigate the effect of turbulent intensity on combustion characteristics, new flame factor model was developed. The principal study is the evaluation of interaction of swirl, tumble and unstrutural component of flow characteristics and correlation between turbulent intensity and flame factor. Computational and experimental study has been, performed such as quasi-dimensional cycle simulation, three dimensional flow analysis, engine performance test and diagnostic simulation. From these studies, it was found that flame factor was a function of engine speed and turbulent intensity.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.