• The Journal of Engineering Geology
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• v.33 no.4
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• pp.689-699
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• 2023

Assessing ground stability is critical to the construction of underground transportation infrastructure. Surface displacement is a key indicator of ground stability, and can be measured using interferometric synthetic aperture radar (InSAR). This study measured time-series surface displacement using permanent scatterer InSAR applied to Sentinel-1 SAR images acquired from January 2017 to June 2023 for the area around a deep underground expressway under construction to connect Mandeok-dong and Centum City in Busan, South Korea. Regions of seasonal subsidence and uplift were identified, as were regions with severe subsidence after summer 2022. To evaluate stability of the ground in the construction area, the mean displacement velocity, final surface displacement, cumulative surface displacement, and difference between minimum and maximum surface displacement were analyzed. Considering the time-series surface displacement characteristics of the study area, the difference between minimum and maximum surface displacement since June 2022 was found to be the most suitable parameter for evaluating ground stability. The results identified highly unstable ground in the construction area as being to the north of the mid-lower reaches of the Oncheon-cheon River and to the west of the Suyeong River at the point where both rivers meet, with the difference between minimum and maximum surface displacement of 40~60 mm.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.164.1-164.1
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• 2011

Previously, many researchers assessed nearshore wave energy in two ways. The first is a simulation with respect to the offshore wave time series to validate the wave buoy data and the wave model results, and the other is to simulate the representative waves of typical seasonal wave conditions. The former requires enormous computational time and effort. The latter yields inspection on the patterns for the spatial and temporal distribution of nearshore wave energy but tends to underestimates the amount of wave energy in the nearshore region owing to the correlation between the significant wave height and wave period. $\ddot{O}$zger et al. (2004) derived the stochastic wave energy formulation by introducing a correction factor explicitly in terms of the covariance of the wave energy and significant wave height. In this study, a correction factor was applied for the assessment of nearshore wave energy obtained by numerical simulation of wave transformation with respect to representative waves.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.199-205
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• 2011

The Saemangeum embankment construction have changed the flowing on the topography of the coastal marine environment. However, the variety of ecological factors are changing from outside of Saemangeum embankment area. The ecosystem of various marine organisms have led to changes by sea surface temperature. The aim of this study is to monitoring of sea surface temperature(SST) changes were measured by using thermal infrared satellite imagery, MODIS and Landsat. The MODIS data have the high temporal resolution and Landsat satellite data with high spatial resolution was used for time series monitoring. The extracted informations from sea surface temperature changes were compared with the dyke to allow them inside and outside of Saemangeum embankment. The spatial extent of the spread of sea water were analyzed by SST using MODIS and Landsat thermal channel data. The difference of sea surface temperature between inland and offshore waters of Saemangeum embankment have changed by seasonal flow and residence time of sea water in dyke.

• Journal of The Geomorphological Association of Korea
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• v.18 no.3
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• pp.11-20
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• 2011

This study is quantitative landscape resources evaluation using Google panoramio geotagged photo collections. Panoramio geotagged photos are including the location information and time information. That is the valuation of the landscape resources and time series analysis allows. And Seasonal changes in preference for a particular landscape can be identified. This study used data from the general public has been produced by the Web 2.0 environment. So, the proposed method in this study will be complement the method using surveys and expert.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.275-284
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• 2010

In this study, we introduce filters that used for the prior adjustment of the holiday effect in seasonal adjustment. And we propose new filters having more various and flexible patterns than conventional ones. Under the practical assumption that patterns of effects before and after the holiday are different, we compare adjustment effect of the proposed filters and the existing ones. In comparison study, we estimate the effect from all possible combinations of shapes of filter by RegARIMA. And then, to adjust holiday effect, we apply the estimated results to time series data of industrial production and shipment index data in South Korea.

• Ocean and Polar Research
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• v.34 no.2
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.384-393
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• 2012

Saemangeum lake is an artificial lake created by reclamation works and an estuary embankment since 2006. The sea water flows into the lake by the operation of two sluice gates, and the freshwater enters into the lake by the upper streams. For the reflection of hydrology and hydrodynamics effects in Saemangeum area, a hydrodynamics model was developed by connecting Hydrological Simulation Program with Fortran (HSPF) and Environmental Fluid Dynamic Code (EFDC). The HSPF was applied to simulate the freshwater discharge from the upper steam watershed, and the EFDC was performed to compute water flow, water temperature, and salinity based on time series from 2008 to 2009. The calibration and validation are performed to analyze horizontal and vertical gradients. The horizontal trend of model simulation results is reflected in the trend of observed data tolerably. The vertical trend is conducted an analysis of seasonal comparisons because of the limitation of vertically observed data. Water temperature reflects on the seasonal changes. Salinity has an effect on the near river input spots. The impact area of salinity is depending on the sea water distribution by gate operation, mainly.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.3
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• pp.235-240
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• 1996

Forecasts of monthly landings of walleye pollock, Theragra chalcogramma, in Korea were carried out by the seasonal Autoregressive Integrated Moving Average(ARlMA) model. The Box - Cox transformation on the walleye pollock catch data handles nonstationary variance. The equation of Box - Cox transformation was Y'=($Y^0.31$_ 1)/0.31. The model identification was determined by minimum AIC(Akaike Information Criteria). And the seasonal ARlMA model is presented (1- O.583B)(1- $B^1$)(l- $B^12$)$Z_t$ =(l- O.912B)(1- O.732$B^12$)et where: $Z_t$=value at month t ; $B^p$ is a backward shift operator, that is, $B^p$$Z_t$=$Z_t$-P; and et= error term at month t, which is to forecast 24 months ahead the walleye pollock landings in Korea. Monthly forecasts of the walleye pollock landings for 1993~ 1994, which were compared with the actual landings, had an absolute percentage error(APE) range of 20.2-226.1 %. Thtal observed annual landings in 1993 and 1994 were 16, 61OM/T and 1O, 748M/T respectively, while the model predicted 10, 7 48M/T and 8, 203M/T(APE 37.0% and 23.7%, respectively).

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.21-29
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• 2019

This study analyzed the variability of drought risk based on trend analysis of dry-seasonal dam inflow located in upper river basins. To this, we used areal averaged precipitation and dam inflow of three upper river dams such as Soyang dam, Chungju dam, and Andong dam. We employed Mann-Kendall trend analysis and change point detection method to identify the significant trends and changing point in time series. Our results showed that significant decreasing trends (95% confidence interval) in dry-seasonal runoff rates (= dam inflow/precipitation) in three-dam basins. We investigated potential causes of decreasing runoff rates trends using changes in potential evapotranspiration (PET) and precipitation indices. However, there were no clear relation among changes in runoff rates, PET, and precipitation indices. Runoff rate reduction in the three dams may increase the risk of dam operational management and long-term water resource planning. Therefore, it will be necessary to perform a multilateral analysis to better understand decreasing runoff rates.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.