• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• Korean Management Science Review
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• v.1
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• pp.68-80
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• 1984

Box-Jenkins 시계열 분석법은 변수에 관한 정보가 부족하거나 너무 많은 변수가 영향을 미치고 있는 경우에도 과학적인 예측치를 구할 수 있는 단기예측 방법이다. Box-Jenkins 모형은 자동회귀 모형(Autoregressive Model), 이동평균 모형 (Moving average Model), 계절적 시계열 모형을 통합한 일반적인 모형이기 때문에 특별한 불안정성을 보이지 않는 경우에는 모두 모형화 할 수 있으며, 모형에 관계된 계수의 수를 최소화 하면서 만족스러운 모형을 찾을 수 있다. Box-Jenkins예측방법은 모형선정, 매개변수추정, 적합성 검정의 3단계를 반복으로 수행함으로써 최적모형에 이르게 하게 하고 있기 때문에 최소의 가능한 모형으로부터 시작하여 부적당한 부분을 제거시켜 나감으로써 시행착오의 과정을 최소화 할 수 있다. 일반 사용자가 Box-Jenkins 시계열 분석법을 쉽게 사용할 수 있도록 Box-Jenkins Package가 개발되었으며 여기서는 KAIST 전산 개발 센터에 설치된 Package를 소개하고 그 사용예를 보였다.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.41-47
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• 2009

This study deals with the side-swipe accidents of 4-legged signalized intersections in Cheongju. The objectives are to analyze the characteristics of the accidents and to develop the related models. In pursuing the above, this study gives particular emphasis to finding the appropriate methodology to modelling. The main results are as follows. First, injuries were analyzed to be twice than property-only accidents in the side-swipe accidents. The accidents were evaluated to occur more in inside-intersection. Also, the accidents were analyzed to be almost the auto-related accidents and to be occurred by the unsafely-driving activity. Second, multiple linear regression models were evaluated to be more statistically significant than multiple non-linear. The most fitted models were analyzed to be the models with the number of accidents as the dependent variable. The factors of side-swipe accidents analyzed in this study were ADT, area of intersection, right-turn-only-lane, number of pedestrian crossings, limited speed of main road, maximum grade and number of signal phase.

• Proceedings of the Korea Water Resources Association Conference
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• 1994.02a
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• pp.361-366
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• 1994

저수지 운영방안 정책결정에 있어 보다 효과적으로 적용할 수 있는 방법론의 개발이 여러측면에서 이루어져 왔다. 이중 동적계획기법의 Explicit 및 Implicit 해에 의한 최적운영방안의 검토가 한강수계의 충주댐을 대상으로 이루어졌다. 이들 방법은 한정된 과거 기록치로부터 합성유량을 발생하여 동적계획기법에 의한 충주댐 최적 운영모형에 적용하여 얻어진 상태변수 및 결정변수의 상관관계를 기준으로 도출한 운영율에 기초하여 모의운영모형을 개발할 수 있다. 개발된 모형중 Explicit 기법은 조건확율에 따른 전단계의 이산화된 유입량 조건별 운영단계의 월초저류량을 기준으로 월말 저류량은 결정하는 방법이며, Implicit 기법은 전단계 저류량 및 유입량, 운영단계 저류량 및 유입량을 대상으로 조합에 의한 회귀분석후 상관성이 우수한 운영율 방정식을 개발하게 된다. 본 연구에서는 이렇게 개발된 두가지 운영율을 기준으로 다목적 운영정책 결정을 위한 저수지 모의운영 모형을 개발하여 모형의 이행도를 평가하였다. Explicit 및 Implicit 기법에 기초한 모의모형의 평가방법은 모의치와 과거 운영실적을 비교하는 것으로 하고 Explicit 기법의 적용에서 홍수기 수문사상의 불확실성에 따른 저수지 운영 효율개선을 위하여 수정 방류량 결정방법을 도입하여 가장 적절한 저수지 운영모의모형 개발방법을 제시하고 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.358-358
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• 2021

자연재해 중 홍수는 전 세계적으로 가장 큰 인적 및 물적 피해를 발생시키고 있으며, 지구온난화로 가속화되고 있는 기후변화는 더욱 극심한 호우와 태풍 현상을 야기하고 있다. 최근 우리나라에서도 2020년 장마는 역대 가장 긴 장마로 기록되는 등 변화된 기상현상으로 인해 홍수피해의 빈도와 강도가 지속적으로 증가하고 있다. 따라서, 이상기후로 인한 홍수피해에 대한 대비와 적응을 위해 위험도 평가, 예·경보시스템, 대피체계 등과 같은 비구조적 대책의 수립이 필요하다. 그 중 홍수피해에 대한 위험도 평가는 과거 홍수피해자료를 바탕으로 지역별 피해양상이나 상대적인 피해위험도를 파악할 수 있으므로 홍수피해 저감대책 수립에 중요한 비구조적 도구로 인식되고 있다. 이에 따라 본 연구는 행정구역별 과거 강우특성 및 홍수피해자료를 분석하여 강우조건에 따라 예상되는 홍수피해위험도를 평가하는 방법을 제안하고자 한다. 이를 위해 먼저, 국민재난안전포털에서 제공하는 재해연보에서 행정구역별 최근 20년 동안의 호우 및 태풍으로 인한 피해자료를 수집하여 인적 및 물적 피해특성 자료를 구축하고, 홍수피해가 발생한 기간에 대해 기상청에서 제공하는 시강우량 자료를 수집하여 홍수피해 사상별 다양한 강우특성자료를 구축한다. 구축된 자료를 이용하여 행정구역별 강우-피해 상관분석을 수행하고, 회귀분석 과정에서 이상치가 존재할 경우 회귀모형의 적합도를 향상시키기 위해 이상치를 제거하고 분석하여, 회귀식의 결정계수 및 유의성 검정결과를 바탕으로 3가지 원인별(호우, 태풍, 종합), 2가지 홍수피해별(인적, 물적) 강우-피해 최적 회귀함수를 선정한다. 최종적으로 강우조건에 따른 홍수피해 규모를 예측하고, 이를 통하여 행정구역별 상대적인 홍수피해위험도를 평가한다. 본 연구를 통해 행정구역별 강우조건에 따른 예상 홍수피해위험도를 분석하여 홍수피해에 대한 저감대책 수립에 기초자료를 제공할 수 있을 것으로 기대된다.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.677-686
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• 2018

There remain numerous ungauged watersheds in Korea owing to limited spatial and temporal streamflow data with which to estimate hydrological model parameters. To deal with this problem, various regionalization approaches have been proposed over the last several decades. However, the results of the regionalization models differ according to climatic conditions and regional physical characteristics, and the results of the regionalization models in previous studies are generally inconclusive. Thus, to improve the performance of the regionalization methods, this study attaches hydrological model parameters obtained using a spatial proximity model to the explanatory variables of a regional regression model and defines it as a hybrid regionalization model (hybrid model). The performance results of the hybrid model are compared with those of existing methods for 37 test watersheds in South Korea. The GR4J model parameters in the gauged watersheds are estimated using a shuffled complex evolution algorithm. The variation inflation factor is used to consider the multicollinearity of watershed characteristics, and then stepwise regression is performed to select the optimum explanatory variables for the regression model. Analysis of the results reveals that the highest modeling accuracy is achieved using the hybrid model on RMSE overall the test watersheds. Consequently, it can be concluded that the hybrid model can be used as an alternative approach for modeling ungauged watersheds.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.733-745
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• 2017

Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, $L_2$-norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.279-293
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• 2021

In this paper, we introduce how to construct sufficient conditions for the oracle property in penalized linear regression model. We give formal definitions of the oracle estimator, penalized estimator, oracle penalized estimator, and the oracle property of the oracle estimator. Based on the definitions, we present a unified way of constructing optimality conditions for the oracle property and sufficient conditions for the optimality conditions that covers most of the existing penalties. In addition, we present an illustrative example and results from the numerical study.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.43-49
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• 2010

This study deals with the traffic accident of the Cheongju arterial link sections. The purpose of the study is to develop the traffic accident model. In pursuing the above, this study gives particular attentions to developing the ZAM(zero-altered model) model using the accident data of arterial roads devided by 322 small link sections. The main results analyzed by ZIP(zero inflated Poisson model) and ZINB(zero inflated negative binomial model) which are the methods of ZAM, are as follows. First, the evaluation of various developed models by the Vuong statistic and t statistic for overdispersion parameter ${\alpha}$ shows that ZINB is analyzed to be optimal among Poisson, NB, ZIP(zero-inflated Poisson) and ZINB regression models. Second, ZINB is evaluated to be statistically significant in view of t, ${\rho}$ and ${\rho}^2$ (0.63) values compared to other models. Finally, the accident factors of ZINB models are developed to be the traffic volume(ADT), number of entry/exit and length of median. The traffic volume(ADT) and the number of entry/exit are evaluated to be the '+' factors and the length of median to be '-' factor of the accident.