• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.461-471
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• 2012

The purpose of this paper is to visualize the housing market dynamics with ESDA (Exploratory Spatial Data Analysis) using MATLAB toolbox, in terms of the modeling housing market dynamics in the Busan Metropolitan City. The data are used the real housing price transaction records in Busan from the first quarter of 2006 to the second quarter of 2009. Hedonic house price model, which is not reflecting spatial autocorrelation, has been a powerful tool in understanding housing market dynamics in urban housing economics. This study considers spatial autocorrelation in order to improve the traditional hedonic model which is based on OLS(Ordinary Least Squares) method. The study is, also, investigated the comparison in terms of $R^2$, Sigma Square(${\sigma}^2$), Likelihood(LR) among spatial econometrics models such as SAR(Spatial Autoregressive Models), SEM(Spatial Errors Models), and SAC(General Spatial Models). The major finding of the study is that the SAR, SEM, SAC are far better than the traditional OLS model, considering the various indicators. In addition, the SEM and the SAC are superior to the SAR.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.807-817
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• 2014

This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.2 s.2
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• pp.115-121
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• 2001

The purpose of the synthetic generation of monthly river flows based on the short term observed data by means of stochastic models is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, a multivariate autoregressive model has been applied to generate monthly flows of the multi sites considering the correlations between each site. The model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness and correlation coefficients. The results of this study showed that the modeled generated flows were statistically similar to the historical flows.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.65-76
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• 2014

Housing prices are influenced by external shock factors such as real estate policy or economy. Thus, the intervention effect is important for the development of forecasting model for housing price index. In this paper, we examined the degree of effective power of external shock factors for forecasting housing price index and analyzed time series models for efficient forecasting of housing price index. It is shown that intervention models are better than other models in forecasting results using real data based on the accuracy criteria.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1155-1168
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• 2016

This paper considers a probability density estimation problem of climate values. In particular, we focus on estimating probability densities of summer extreme temperature over South Korea. It is known that the probability density of climate values at one location is similar to those at near by locations and one doesn't follow well known parametric distributions. To accommodate these properties, we use a mixture of conditional autoregressive species sampling model, which is a nonparametric Bayesian model with a spatial dependency. We apply the model to a dataset consisting of summer maximum temperature and minimum temperature over South Korea. The dataset is obtained from University of East Anglia.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.61-69
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• 2015

This study compares the forecasting accuracy of five econometric models on domestic total freight volume in South Korea. Applied five models are as follows: Ordinary Least Square model, Partial Adjustment model, Reduced Autoregressive Distributed Lag model, Vector Autoregressive model, Time Varying Parameter model. Estimating models and forecasting are carried out based on annual data of domestic freight volume and an index of industrial production during 1970~2011. 1-year, 3-year, and 5-year ahead forecasting performance of five models was compared using the recursive forecasting method. Additionally, two forecasting periods were set to compare forecasting accuracy according to the size of future volatility. As a result, the Time Varying Parameter model showed the best accuracy for forecasting periods having fluctuations, whereas the Vector Autoregressive model showed better performance for forecasting periods with gradual changes.

• Proceedings of the Korea Water Resources Association Conference
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• 1993.07a
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• pp.409-415
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• 1993