• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.294-294
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• 2012

홍수위험도 추정에 있어서 불확실성은 수리, 수문, 구조, 환경 및 사회경제적인 불확실성과 관련 있으며, 수리 수문학적 불확실성은 주로 수리 수문학적 현상과 그 과정에 대한 불완전한 지식, 그리고 그 과정에 포함된 매개변수들에 대한 불완전한 지식과 관련이 있다. 이러한 여러 가지 불확실성은 홍수위험도 추정에 있어서의 불확실성에 중요한 요인으로 작용하므로 불확실성을 설명하기 위한 통계적 정보는 신뢰성 있는 홍수위험도 추정에 있어서 선행조건이라 할 수 있다. 이러한 불확실성 요인중 강우의 공간분포에 대한 신뢰성 있는 추정은 수자원 해석 및 설계에 있어서 필수적인 요소이다. 강우장의 공간변동성에 대한 고해상도 추정은 홍수, 특히 돌발홍수의 원인이 되는 국지성 호우의 확인 및 분석에 있어서 중요하다. 또한 강우의 공간 변동성에 대한 고려는 면적평균강우량 추정의 정확도를 향상시키는데 있어서 중요하며, 강우-유출모델의 모의결과에 대한 신뢰도를 향상시키는데 큰 영향을 미친다. 최근 공간자료에 대한 공간분포예측에 있어서 공간상관성을 고려할 수 있는 공간통계학적 기법의 적용이 증가하고 있으며, 이러한 공간통계학적 기법의 적용에 있어서 신뢰성 있는 모델 매개변수의 추정 및 불확실성 평가는 공간분포 예측결과에 대한 신뢰성을 향상시키는데 중요한 역할을 한다. 외국의 경우 공간분포예측 및 모의, 매개변수의 불확실성 평가 등과 관련하여 활발한 연구가 이루어지고 있는 반면 국내 수자원 분야에서는 아직까지 활발한 연구가 이루어지고 있지 않은 실정이다. 국내의 수문설계실무에서와 같이 확률홍수량을 강우빈도분석과 강우-유출모델을 이용하여 추정할 경우 확률홍수량 추정에 있어서 확률강우량 및 공간분포에 대한 불확실성과 강우-유출모델에서의 불확실성이 확률홍수량 추정에서의 불확실성에 영향을 미치며, 이후 연피해기대치 추정과 같은 홍수위험도 추정의 불확실성에도 영향을 미치게 된다. 따라서 본 연구에서는 강우공간분포의 불확실성을 고려한 홍수량 추정을 위하여 공간추계모의 기법인 CEM을 적용하여 강우공간분포의 불확실성을 정량화하고 강우-유출모델의 입력 강우량에 대한 확률분포를 추정하였다. 강우-유출해석의 경우 유효우량 및 홍수수문곡선 산정을 위하여 국내 수자원 실무에서 가장 많이 적용되고 있는 NRCS CN 기법, Clark 및 Muskingum 모델을 적용하였다. 이로부터 강우공간분포의 불확실성 추정, 소유역별 입력 강우량에 대한 확률분포의 추정 및 재현기간별 확률홍수량의 불확실성 정량화 방안을 제시하였다. 이러한 결과들은 풍수해저감대책, 유역종합치수대책 등 각종 수자원 계획 및 설계실무에서 확률홍수량 및 홍수 또는 재해위험도 추정의 신뢰성을 향상시킬 수 있는 방법론적 대안으로 활용될 수 있을 것으로 판단된다.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.220-233
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• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.40-48
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• 2005

This paper will specify geo-objects and geo-fields of the geo-ecological contamination source and implement the system for evaluating an ocean Environmental contamination based on the spatial statistical analysis. In order to produce the grade of fishery that can evaluate the ocean effect, we will analysis the degree of the spatial correlation by semi-veriogram and predicate the elevation raster of spatial data using ordinary kriging method. This paper is to estimate the grade of fishery contamination region and produce the ratio of the area according to the fishery grade. Therefore, we can contribute to produce fishery grade that evaluates the ocean effect by means of deciding an efficient fishery environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.900-903
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• 2005

본 연구에서는 기상레이더의 원시자료를 홍수해석분야에 적용하기 위해서 먼저 3차원 공간상에서 구면좌표계값을 가지는 레이더 자료를 직교 좌표계로 변환하기 위한 CAPPI 산출 프로그램을 개발하였다. 개발된 모형은 CAPPI 자료의 이용목적에 따라서 다양한 해상도 및 고도에 대한 자료가 필요할 수 있다는 생각하에 임의의 해상도 및 고도 등에 대한 옵션을 사용자가 임의로 지정함으로서 빠르고 효율적으로 계산할 수 있도록 구성하였다. 기상레이더와 연계한 개선된 강우량 자료를 산정하기 위해서 cokriging 기법을 적용하였는데, 강우의 공간적 분포양상 및 경계를 정확하게 묘사할 수 있는 레이더 자료와 한 지점에서 좋은 정확도를 가지는 강우량 자료를 조합한 2차원 정량강우량 산정을 위해서 지형통계학적 분석을 실시하였다. 2002년 태풍 루사에 대해서 본 기법을 적용하였으며, 산정된 2차원 정량강우량은 유역에서의 실시간 홍수량을 산정하기 위해서 이용되었다. 본 연구에서는 기상-강우-유역홍수량 산정에 이르는 통합홍수해석을 실시함으로서 유역 유출량 산정에 대한 새로운 개선된 방향을 제시하고자 한다.

• Journal of Industrial Convergence
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• v.21 no.7
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• pp.29-39
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• 2023

In the recent period, there have been numerous earthquakes both domestically and internationally, and buildings in South Korea are particularly vulnerable to seismic design and earthquake damage. Therefore, the objective of this study is to discover an effective method for assessing the seismic vulnerability of buildings and conducting a density analysis of high-risk structures. The aim is to model this approach and validate it using data from pilot area(Seoul). To achieve this, two modeling techniques were employed, of which the predictive accuracy of the statistical analysis technique was 87%. Among the machine learning techniques, Random Forest Model exhibited the highest predictive accuracy, and the accuracy of the model on the Test Set was determined to be 97.1%. As a result of the analysis, the district rating revealed that Gwangjin-gu and Songpa-gu were relatively at higher risk, and the density analysis of at-risk buildings predicted that Seocho-gu, Gwanak-gu, and Gangseo-gu were relatively at higher risk. Finally, the result of the statistical analysis technique was predicted as more dangerous than those of the machine learning technique. However, considering that about 18.9% of the buildings in Seoul are designed to withstand the Seismic intensity of 6.5 (MMI), which is the standard for seismic-resistant design in South Korea, the result of the machine learning technique was predicted to be more accurate. The current research is limited in that it only considers buildings without taking into account factors such as population density, police stations, and fire stations. Considering these limitations in future studies would lead to more comprehensive and valuable research.

• Spatial Information Research
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• v.9 no.2
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• pp.239-261
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• 2001

A large suite of official statistical data sets has been compiled for geographical units under the national directives, and it is the quantitative regional analysis procedures that could add values to them. This paper reports our attempts at prototyping a statistical GIS which is capable of serving over the Web a variety of regional analysis routines as well as value-added statistics and maps. A pilot database of some major statistical data was ingested for the city of Seoul. The baseline subset of regional analysis methods of practical usage was selected and accommodated into the business logic of the target system, which ranges from descriptive statistics, regional structure/inequality measures, spatial ANOVA, spatial (auto) correlation to regression and residual analysis. The leading-edge information technologies including the application server were adopted in the system design and implementation so that the database, analysis modules and analytic mapping components may cooperate seamlessly behind the Web front-end. The prototyped system supports tables, maps, and files of downloadable format for input and output of the analyses. One of the most salient features of out proposed system is that both the database and analysis modules are extensible via the bi-directional interface for end users; The system provides users with operators and parsers for algebraic formulae such that the stored statistical variables may be transformed and combined into the newly-derived set of variables. This functionality eventually leads to on-the-fly fabrication of user-defined regional analysis algorithms. The stored dataset may also be temporarily augmented by user-uploaded dataset; The extension of this form, in essence, results in a virtual database which awaits for users commands as usual. An initial evaluation of the proposed system confirms that the issues involving the usage and dissemination of information can be addressed with success.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2004.04a
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• pp.157-165
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• 2004

• Computational Structural Engineering
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• v.28 no.2
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• pp.58-63
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• 2015

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.25-45
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• 2020

The primary purpose of this study is to compare and analyze the performance of local spatial autocorrelation techniques in identifying spatial distribution patterns of green spaces. To achieve the objective, this researcher uses satellite image analysis and spatial autocorrelation techniques. The result of the study shows that the LISA cluster map with the spatial outlier cluster is superior to other analytical methods in identifying the spatial distribution pattern of urban green space. This study can contribute to the related fields in that it uses several different research methods than the existing ones. Despite this differentiation and usefulness, this study has limitations in using low-resolution satellite imagery and NDVI among vegetation indices in identifying spatial distribution patterns of green areas. These limitations may be overcome in future studies by using UAV images or by simultaneously using several vegetation indices.