• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.348-348
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• 2022

미래에 대한 기후는 과거와 비교하여 변동성이 더 크고 불확실성 또한 더 크기 때문에 미래의 기후변화를 예측하기 위해서는 기후변화의 절대적인 크기뿐 아니라 불확실한 정도도 함께 고려되어야 한다. 본 연구에서는 CMIP6(Coupled Model Intercomparison Project Phase 6) DB에서 제공된 일 단위 18개의 GCMs(General Circulation Models)의 결과를 분석하였으며 또한 3개의SSP(Shared Socioeconomic Pathway)시나리오와 3개의 미래 구간에 대하여 100개의 앙상블을 각각 생성하였다. 불확실성을 초래하는 원인을 3가지로 구분하고, 각각의 원인에 대한 불확실성의 정도를 앙상블 시나리오에 반영하고자 한다. 현재 기간 및 미래 기간에 대해 100개의 20년 시계열 날씨변수 앙상블을 생성하여 LSTM(Long short-term memory)의 입력자료로 사용하여 댐유입량, 저수위, 방류량을 산정하였다. 댐 유입량 및 방류량의 예측성능을 향상시키기 위해 Input predictor의 종류를 선정하는 방법과 그 변수들의 lag time을 결정하는 방법, 입력자료들을 재구성하는 방법, 하이퍼 매개변수를 효율적으로 최적화하는 방법, 목적함수 설정 방법들을 제시하여 댐 유입량 및 방류량의 예측을 크게 향상시키고자 하였다. 본 연구에서 예측된 미래의 댐유입량 및 방류량 정보는 홍수 또는 가뭄 등 다양한 수자원 관련 문제의 전략을 수립하는 데 있어서 적절한 도움이 될 것이다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.295-304
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• 2020

The IPCC (Intergovernmental Panel on Climate Change) recommended the importance of preventive measures against extreme weather, and heat waves are one of the main themes for establishing preventive measures. In this study, we tried to analyze the heat vulnerable areas by considering not only spatial characteristics but also social characteristics. Energy consumption, popu lation density, normalized difference vegetation index, waterfront distance, solar radiation, and road distribution were examined as variables. Then, by selecting a suitable model, SLM (Spatial Lag Model), available variables were extracted. Then, based on the Fuzzy theory, the degree of vulnerability to heat waves was analyzed for each variable, and six variables were superimposed to finally derive the heat vulnerable area. The study site was selected as the Daegu area where the effects of the heat wave were high. In the case of vulnerable areas, it was confirmed that the existing urban areas are mainly distributed in Seogu, Namgu, and Dalseogu of Daegu, which are less affected by waterside and vegetation. It was confirmed that both spatial and social characteristics should be considered in policy support for reducing heat waves in Daegu.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.446-453
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• 2018

The study examines corporate research & development (R&D) expenditure in modern finance. Firms may face one of the essential issues to maintain their optimal levels of R&D expenditures in order to increase corporate profit. Accordingly, financial determinants that may influence R&D spending are statistically tested for firms listed on the KOSPI stock market during the period from 2010 to 2015. Financial determinants which may discriminate between firms in high-growth and low-growth industries are examined on a relative basis. Explanatory variables including one-period lagged R&D expenses (Lag_RD), cross-product term between the Lag_RD and type of industry (as a dummy variable), and advertising expenses (ADVERTISE) significantly influenced corporate R&D intensity. Moreover, high-growth firms in domestic capital markets showed higher Lag_RD, profitability (PROF) and foreign equity ownership (FOS) than their counterparts in low-growth sectors, whereas low-growth firms had higher market-value based leverage (MLEVER) and ADVERTISE. Overall, these results are expected to influence decision-making of firms concerning the optimal level of R&D expenditure, which may in turn enhance shareholder wealth.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.997-1012
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• 2006

We have presented a nonparametric stochastic approach for the SOI(Southern Oscillation Index) series that used nonlinear methodology called Nonlinear AutoRegressive(NAR) based on conditional kernel density function and CAFPE(Corrected Asymptotic Final Prediction Error) lag selection. The fitted linear AR model represents heteroscedasticity, and besides, a BDS(Brock - Dechert - Sheinkman) statistics is rejected. Hence, we applied NAR model to the SOI series. We can identify the lags 1, 2 and 4 are appropriate one, and estimated conditional mean function. There is no autocorrelation of residuals in the Portmanteau Test. However, the null hypothesis of normality and no heteroscedasticity is rejected in the Jarque-Bera Test and ARCH-LM Test, respectively. Moreover, the lag selection for conditional standard deviation function with CAFPE provides lags 3, 8 and 9. As the results of conditional standard deviation analysis, all I.I.D assumptions of the residuals are accepted. Particularly, the BDS statistics is accepted at the 95% and 99% significance level. Finally, we split the SOI set into a sample for estimating themodel and a sample for out-of-sample prediction, that is, we conduct the one-step ahead forecasts for the last 97 values (15%). The NAR model shows a MSEP of 0.5464 that is 7% lower than those of the linear model. Hence, the relevance of the NAR model may be proved in these results, and the nonparametric NAR model is encouraging rather than a linear one to reflect the nonlinearity of SOI series.

• International Area Studies Review
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• v.16 no.1
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• pp.3-27
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• 2012

This paper adopts recently developed panel unit root test that is cross-sectionally robust. Cointegration test is also used to find whether regional house prices are in line with gross regional domestic production (GRDP) in the long run in Korea during 1989-2009. Based on the panel VECM and the panel ARDL models, we examine causal relationships among the variables and estimate the long-run elasticity. We find evidence of cointegration and bidirectional causal relationships between regional house prices and GRDP. The results of long-run estimates, using both fixed effect and ARDL models, show that house prices positively and significantly influence on the GRDP and vice versa. Together with these results, the findings of ARDL-ECM imply that there exists a long-run equilibrium relationship between house prices and regional economic variables even if there is a possibility of short-run deviation from its long-run path.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.1-13
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• 2010

For the first time, this study identifies Nash model parameters by GIUH theory based on grid of GIS with heterogeneity of drainage path. Identified parameters have advantages to improve accuracy and usefulness with considering hillslpoe-flow, geomorphological dispersion and easily extracting geomorphological factors by GIS in the watershed. Calculated results by identified parameters compare with observation data for verification of this model. The comparison is well correspondence between observed data and calculated results. And the comparison results of changing trends about lag time and the variance as hillslope and channel characteristic velocities sensitively present changes about hillslope characteristic velocity. Thus this model justifies that estimation of hillslope characteristic velocity demands with the great caution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.872-880
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• 2017

Usually, on/off control method is a way to control the thruster. Bang-Bang Control, PWM(Pulse Width Modulator) and PWPF(Pulse Width Pulse Frequency) are widely used as a typical way. When we are designing PWPF, the incorrectly designed parameters($K_m$, ${\tau}$, $U_{on}$, $U_{off}$, $U_m$) make trouble, such as the phase lag, the wasted fuel, the reduced system life. Therefore, the effect of parameters on the system performance should be analyzed before the proper parameters are selected. In this paper, we suggest the PWPF parameters design method by performing a static analysis, and analyze the interactive effects on design parameters by performing a dynamic analysis and simulation.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.655-660
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• 2009

As the global economic crisis, the Korean foreign exchange market appears unstable with large fluctuations in exchange rate. Inevitably, there is growing attention on price variables such as exchange rate and interest rates and also on corelation between the factors. This is an empirical study on the causality of fluctuation between exchange rate and interest rate in the Korean market under global economic crisis. The fluctuations in won/dollar exchange rate and call rate are described and followed by analysis of lead-lag relationship between the two variables using Cross-correlation function and Granger causality test.