• Journal of Environmental Health Sciences
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• v.22 no.4
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• pp.55-61
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• 1996

Measurements of the seasonal variations of concentration and size distribution of TSP, $SO_4^{2-}, NO_3^-, Cl^-, NH_4^+, Na^+, K^+, Ca^{2+}$ and $Mg^{2+}$ were made by Andersen air sampler from May 1995 to April 1996 in Seoul. The size distribution of these ions was divided into four patterns. 1) Distribution was concentrated on fine particles over a year such as $NO_3^-$ component, 2) Distribution was predominated in coarse particles fraction over a year such as $Mg^{2+}$ and $Ca^{2+}$ components, 3) Distribution was differerent from various seasons such as $NH_4^+, SO_4^{2-}, Cl^-$ and $K^+$ components, 4) Distribution was bi-modal such as $Na^+$.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.985-994
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• 2005

We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.36 no.6
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• pp.676-685
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• 2003

Seasonal variation of the fishery resource in the shallow waters of Ganghwado tidal flat was investigated using monthly collected samples with a fence net from June 1998 to May 1999. Thirty-six species were found including 27 fish species, 6 crustaceans, and 3 molluscs. Of the fish, Konosirus punctatus, Sardinella zunasi Liza haematocheila and Synechogobius hasta dominated in the number of individuals $(92.1\%)$ and in biomass $(94.5\%).$ A few number of resident species, such as L. haematocheila and S. hasta, were collected only during the cold months. As the water temperature increased in the spring, the adult migratory fish such as K. punctatus and S. zunasi, were collected. In the summer, the juvenile fish recruited in the shallow water showing a peak in fish abundance. The data suggested that they grew until late autumn before moving to deeper waters for over-wintering. The principal component analysis showed that the seasonal variation in species composition was principally determined by water temperature and/or water temperature related factors. The species composition of the fish assemblage in the study area suggested that these species are highly adapted to extreme seasonal temperature variation and high water turbidity.

• The Korean Journal of Ecology
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• v.18 no.1
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• pp.137-146
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• 1995

Acer pseudo-sieboldianum, A. ginnala, A. negundo and A. saccharinum were selected as materials for the studies on the seasonal fluctuation of chlorophyll content and photosynthetic rates. In all the four species during the growing season except in October, the principal component that determined the total chlorophyll content was chlorophyll a. Content of chlorophyll b increased with leaf age, but that of chlorophyll a decreased. In contrast to A. saccharinum and A. pseudo-sieboldianum which showed their maximum chlorophyll content in June, A. negundo, which showed the highest chlorophyll content of the four species, exhibited its maximum chlorophyll content in July, whereas September in the case of A. ginnala. The fluctuation of chlorophyll content was similar to that of air temperature, and it increased till July. But chloprophyll content showed a significant relationship in early stage of leaf development, and there could be any interdependence between them in accordance with the seasonal change, no longer. In all four species light compensation points decreased in accordance with the seasonal change, and the maximum photosynthetic rates were obtained in August. Respiratory rates were shown no significant difference among species, and they decreased according to the adbance of season.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.6
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• pp.311-317
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• 2017

The aim of this study is to investigate the environmental properties of paddy soils depending on farming methods and seasonal variation. The paddy soils in 11 plots of conventional paddy and 24 plots of organic paddy were sampled and analyzed in four season of March, May, August, and October. The obtained data of soil properties were used for statistical analysis. Analysis of variance showed that only $NH_4-N$ and $P_2O_5$ were significantly different depending on farming methods. However, the differences of all soil properties depending on seasonal variation were strongly significant. Principal component analysis also presented that nitrogen and phosphorus concentration in soils were more significantly influenced by seasonal variation than farming method. Electric conductivity in soil was decreased from March to October. Amounts of soil organic matter in August and October were higher than that in March and May. T-N was decreased from March to October. $NH_4-N$ and $NO_3-N$ in the soil of both conventional and organic paddy were higher in May than other seasons. T-P concentration was found to be highest in August, but $P_2O_5$ concentration was lowest in August. It can be concluded that seasonal variation should be considered for analysis and comparison of soil environmental properties.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• Journal of Ecology and Environment
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• v.38 no.2
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• pp.145-156
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• 2015

This study was based on water quality data of the Lake Doam watershed, monitored from 2010 to 2013 at eight different sites with multiple physiochemical parameters. The dataset was divided into two sub-datasets, namely, non-rainy and rainy. Principal component analysis (PCA) and factor analysis (FA) techniques were applied to evaluate seasonal correlations of water quality parameters and extract the most significant parameters influencing stream water quality. The first five principal components identified by PCA techniques explained greater than 80% of the total variance for both datasets. PCA and FA results indicated that total nitrogen, nitrate nitrogen, total phosphorus, and dissolved inorganic phosphorus were the most significant parameters under the non-rainy condition. This indicates that organic and inorganic pollutants loads in the streams can be related to discharges from point sources (domestic discharges) and non-point sources (agriculture, forest) of pollution. During the rainy period, turbidity, suspended solids, nitrate nitrogen, and dissolved inorganic phosphorus were identified as the most significant parameters. Physical parameters, suspended solids, and turbidity, are related to soil erosion and runoff from the basin. Organic and inorganic pollutants during the rainy period can be linked to decayed matters, manure, and inorganic fertilizers used in farming. Thus, the results of this study suggest that principal component analysis techniques are useful for analysis and interpretation of data and identification of pollution factors, which are valuable for understanding seasonal variations in water quality for effective management.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.418-430
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• 2017

The quality of surface water is a very important issue to use various demands like as drinking water, industrial, agricultural and recreational usages. There has been an increasing demand for monitoring water quality of many rivers by regular measurements of various water quality variables. However precise and effective monitoring is not enough, if the acquired dataset is not analyzed thoroughly. Therefore, the aim of this study was to estimate differences of seasonal and regional water quality using multivariate data analysis for each investing tributaries in Han River. Statistical analysis was applied to the data concerning 11 mainly parameters (flow, water temperature, pH, EC, DO, BOD, COD, SS, TN, TP and TOC) for the time period 2012~2016 from 12 sampling sites. The seasonal water quality variations showed that each of BOD, TN, TP and TOC average concentration in spring and winter was higher than that of summer and fall, respectively. In summer each flow rate and average concentration of SS was higher than any other seasons, respectively. The correlation analysis were explained that EC had a strong relationship with BOD (r=0.857), COD (r=0.854), TN (r=0.899) and TOC (r=0.910). According to principal component analysis, five principal components (Eigenvalue > 1) are controlled 98.0% of variations in water quality. The first component included TP, DO, pH. The second component included EC, TN. The third component included SS. The fourth component included flow. The last component included Temp. Cluster analysis classified that spring is similar to fall and winter with water quality parameters. AnyA, WangsA, JungrA and TancA were identified as affected by organic pollution. Cluster analysis derived seasonal differences with investigating sites and better explained the principal component analysis results.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of Korean Society on Water Environment
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• v.25 no.4
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• pp.539-546
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• 2009

We examined the effects of land cover types on water quality based on data surveyed during April 2007-February 2008 from 178 sites of 111 streams in Paldang watershed. BOD, COD, DO, SS, T-N, and T-P concentrations of spring and summer were strongly and significantly associated with the first principal component of the proportions of eight land cover types, and differences between all parameter's concentration except SS and T-N of spring and summer were insignificantly related with them. SS and T-N concentration of summer were significantly correlated with increase and decrease of stream flow. T-P concentration of spring was the most significantly related with the second principal component which was positively correlated with the proportions of residential and forest land covers and was negatively correlated with the proportions of paddy and grass land covers. It is necessary to manage land use of the upper watershed and stream flow for improvement in water quality because seasonal variations of each water quality parameter are dependent upon land cover and flow variations.