• ALGAE
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• v.27 no.3
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• pp.215-224
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• 2012

Growth dynamics of the seagrass, Zostera marina were examined at the two stations (Myungju and Dagu) in Jindong Bay on the southern coast of Korea. Eelgrass leaf productivities, underwater irradiance, water temperature, dissolved inorganic nitrogen (DIN) in water column and sediments, and tissue carbon (C) and nitrogen (N) content were monitored monthly from March 2002 to January 2004. Underwater irradiance fluctuated highly without a clear seasonal trend, whereas water temperature showed a distinct seasonal trend at both study stations. Water column DIN concentrations were usually less than $5{\mu}M$ at both study sites. Sediment pore water $NH_4{^+}$ and $NO_3{^-}+NO_2{^-}$ concentrations were higher at the Myungju site than at the Dagu site. Eelgrass leaf productivity at both study sites exhibited a distinct seasonality, increasing during spring and decreasing during summer. Seasonal variation of eelgrass productivity was not consistent with seasonal patterns of underwater irradiance, or water temperature. Eelgrass tissue C and N content at both study sites also showed significant seasonal variations. Relationships between tissue C and N content and leaf productivities exhibited usually negative correlations at both study sites. These negative correlations implied that the growth of Z. marina at the study sites was probably limited by C and N supplies during the high growth periods.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

• Journal of Environmental Health Sciences
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• v.22 no.2
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• pp.124-129
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• 1996

In order to investigate the deposition chara.cteristics of water-soluble ionic components in dustfall in Kwangju city, dustfall samples were collected by deposit jar for one year from December 1992 to November 1993. The depositjon amount of dustfall and water-soluble ionic components ($SO_4^{2-}, NO_3^-, Cl^-, NH_4^+, Na^+, Ca^{2+}, Mg^{2+}, K^+$) were measured. The total deposition amount of dustfall was 10.0 ton/$km^2$/month and showed seasonal trend of Summer and Spring > Fall and Winter. The total deposition amounts of water-soluble components showed 2.41 ton/$km^2$/month and seasonal trend of Summer > Fall > Spring > Winter. Deposition amount of $SO_4^{2-}$ was 0.99 ton/$km^2$/month which makes up 41% of water-soluble components. The deposition amounts of dustfall and watersoluble components according to the sampling points were approximately similar to each other. From this result, it can be estimated that the deposition amounts of dustfall and water-soluble components in dustfall were more influenced by the seasonal variation than the regional emission characteristics of pollution source. The content of each ionic component to the deposition amount of water-soluble components showed in order of $SO_4^{2-} > Cl^- > NH_4^+ > Na^+ > Ca^{2+} = K^+ > NO_3^- > Mg^{2+}$ respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.2
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• pp.231-245
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• 2003

Hourly PM$_{10}$ concentrations measured at 11 sites in Seoul and 10 sites in the large cities over South Korea for the period from March 1995 to February 2000 are analyzed to examine annual trend and monthly variations of the PM$_{10}$ concentrations. Further analysis has been carried out by using the one year data from March 1999 to February 2000 to see the seasonal variation, diurnal variation and weekly variation of the seasonally averaged PM$_{10}$ concentrations at each site. Weekly variations of the CO concentrations at the same sites for the same one year period are compared with that of the PM$_{10}$ concentration. There is no significant annual trend in the variation of the PM$_{10}$ concentration at all the sites analyzed. The seasonal and monthly mean concentrations show a minimum concentration in summer and alternative maximum concentration in spring and winter for most sites. The diurnal variation of the seasonally averaged mean PM$_{10}$ concentrations is strongly affected by traffic loads and meteorological conditions. The weekly variation of seasonal averaged concentrations of CO and PM$_{10}$ shows a high concentration for weekdays in spring, autumn and winter while high concentration for weekends in summer.nds in summer.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.192-193
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• 2015

Construction Cycle Trend Survey, which survey total value of orders and realized amounts monthly, is a valuable statistics that used to quick grasp or forecast the trend of domestic construction business. In recent periodical survey quality diagnoses, few professional users named a problem that Construction Cycle Trend Survey could not get together with the current state of the construction industry. This study examined weather Construction Cycle Trend Survey reflects the economic sentiment of construction business or not. Paired t test was performed between Construction Cycle Trend Survey and Construction Business Survey Index (CBSI), and significant differences were verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.5
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• pp.510-520
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• 2010

This study investigated the community structure and seasonal variation of the fish assemblages on Jangbong tidal flat, Incheon, Korea. Fish were collected monthly using a small otter trawl from November 1999 to January 2001. Thirty-six fish species were recorded, with a mean density of 185 individuals and biomass of 2,594.3 gWWt. The most abundant species by number were Johnius grypotus (23.7%), Acanthogobius hasta (17.8%), and Cynoglossus joyneri (10.7%), while the dominant species by catch weight were Acanthogobius hasta (21.2%), Sebastes schlegeli (16.2%), J. grypotus (14.0%), and C. joyneri (10.8%). Cluster analysis and non-metric multi-dimensional scaling (nMDS) were applied to assess the seasonal fluctuation in the fish assemblages. Based on the result of the cluster analysis and nMDS ordination, the faunal group could be divided into cold- and warm-water specialist groups. The cold-water specialists included A. hasta, Acanthogobius luridus, Triaenopogon barbatus, Tridentiger trigonocephalus, and Liza haematocheila. The warm-water specialists were J. grypotus, C. joyneri, S. schlegeli, and Hexagrammos otakii. In conclusion, the community structure showed a distinct seasonal trend, which seemed to be related to the seasonal fluctuations in water temperature.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.15-31
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• 2003

In this study, we will construct a statistical model which considered the irregularity of observed time sequence in order to analyze sets of bioindex data gathered from stations in Lake Ontario for a number of years. We fit a linear model to account for the trend and seasonal component in an exploratory way and draw variogram and correlogram for further confirmatory studies.

• Journal of Korea Water Resources Association
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• v.47 no.7
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• pp.643-656
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• 2014

The homogeneity analysis of temporal (monthly, seasonal and annual) climate aridity index trend was accomplished for 43 climate measurement stations in South Korea. Furthermore, 43 stations were grouped into 9 different regions and the temporal and regional homogeneity of climate aridity index trends in each region and entire 9 regions were analyzed. For analysis, monthly, seasonal and annual climate aridity indexes of 43 study stations were estimated using precipitation and potential evapotranspiration calculated from FAO Penman-Monteith equation. The Mann-Kendall statistical test for significant trend was accomplished using the estimated climate aridity indexes and the results of trend test (Z scores) were used to analyze the temporal and regional homogeneity of climate aridity index trends. The study results showed the temporal and regional homogeneity of climate aridity index trends for individual and entire 9 regions. However, the homogeneity and the extent of aridity index trend showed different patterns temporally and regionally.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.111-116
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• 2001

This paper introduces how to adopt the X-12-ARIMA to decompose the railway passenger demand of the Korea National Railroad Especially, selecting on proper filters is focused. The trend filter is identical to the low pass filter in the signal Processing field, and so the seasonal filter is to band pass filter too. Some considerations, selecting a filter, are provided from the view-point of the spectrum analysis. The technique introduced in this paper will be adopted to the project that is to develope the forecasting system of Korea railway passenger demand which is a part of the high speed rail information system.