• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• 한국방재안전학회논문집
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• 제14권1호
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• pp.41-50
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• 2021

하천 제방 변위를 사전에 예측하는 방안으로 본 연구에서는 InSAR 기법 중 Differential Interferometry(D-InSAR) 기법을 이용하여 2020년 여름 발생한 남원시 금곡교(섬진강) 인근의 제방 붕괴 지역에서 취약지점을 확인하였다. 2020년 봄과 여름 각각 5장의 sentinel-1 영상과 위성 영상 전처리 도구인 SNAP을 사용하여 2020년 8월 8일 제방 붕괴 전까지의 발생한 변위를 분석한 결과, 붕괴 발생 지역의 변위 변동성지수(Variation Index), V 가 상대적으로 크게 발생하였으며 이를 통해 붕괴 전조증상을 확인할 수 있었다. 향후에 산출한 변위를 분석한 결과와 유역의 지하수위, 기온, 수위, 토양도 및 토양 수분도와 같은 수문기상학적 요인과 상관관계를 분석하여 하천 제방의 모니터링 시스템을 구축할 수 있다면 기존의 하천 제방 유지·보수 점검 시스템의 많은 한계점을 극복하고 초정밀, 자동화된 하천 제방 유지관리 기술 고도화와 국가 재난관리의 향상이 가능할 것으로 기대한다.

• 농촌계획
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• 제28권1호
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• pp.37-47
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• 2022

The purpose of this study is to introduce the concept of community resilience to rural society and build an index suitable for the reality of rural areas. Furthermore, by calculating the importance of evaluation factors, it was attempted to present priorities and alternatives for each evaluation factor. By stratifying the derived indicators, a survey was conducted targeting 20 researchers, practitioners, and public officials, three groups of experts working in rural areas who were well aware of the realities and problems of rural areas. In the survey, a pairwise comparison was performed to compare factors 1:1 to calculate the importance, and for rational and consistent decision-making, decisions were made in the 9-grade section. Using the collected data, consistency analysis that can evaluate reliability in the decision-making process and the relative weight of evaluation factors were calculated through AHP analysis. As a result of the analysis, as a result of examining the priority of final importance by summarizing the importance of all evaluation factors, 'Income creation using resources' > 'Population Characteristics' > 'Tolerance' > 'External Support' > 'Social Accessibility' > 'Physical Accessibility' > 'Community Competence' > 'Infrastructure' > 'Leader Competence' > 'Natural Environment' was derived in the order. In the study dealing with urban community resilience indicators, social aspects such as citizen participation, public-private cooperation, and governance were presented as the most important requirements, but this study differs in that the 'income creation' factor is derived as the most important factor. This can be seen through the change in the income difference between rural and urban areas. The income structure of rural areas has changed rapidly, and it is now reaching a very poor level, so it is necessary to prepare alternatives to 'income creation' in the case of rural areas. Unlike urban indicators, 'population characteristics' and 'tolerance' were also derived as important indicators of rural society. However, there are currently no alternatives to supplement the vulnerability by strengthening the resilience of rural communities. Based on the priority indicators derived from the study, we tried to suggest alternatives necessary for rural continuity in the future so that they can be supplemented step by step.

• 한국재난정보학회 논문집
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• 제17권4호
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• pp.864-881
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• 2021

연구목적: 본 연구는 빅데이터와 인공지능 기술을 기반으로 다양한 위험 특성과 개개인의 상황을 고려한 맞춤형 예방 솔루션을 제공하는 생활안전 예방서비스 연구개발의 일환으로, 일상 생활안전과 관련하여 개인의 현재 안전수준을 정량적 수치로 나타내는 생활안전지수를 산출하는 방안을 제시하여, 안전사고를 예방하고 대응하기 위한 맞춤형 종합지수 서비스를 제공하는 데 목적이 있다. 연구방법: 본 연구의 핵심이 되는 모델은 AHP(Analysis Hierarchy Process)와 리커트 척도(Likert Scale)를 혼용하는 방법으로, 전문가 그룹의 합의형성 모델을 기반으로 산출된다. 생활안전 예방서비스를 평가할 수 있는 평가항목을 위험지표, 취약지표, 예방지표 등으로 구분하고, 이를 AHP 의사결정 방법론에 따라 AHP 계층구조로 정의하여 각 레벨 항목의 쌍대비교를 통해 평가항목 간 상대적 가중치를 산출하는 방법을 제안한다. 또한 평가항목을 적용한 개별 예방서비스에 대한 평가는 향후 생활안전 예방서비스의 확대를 고려하여 AHP 쌍대비교를 대신하여 리커트 척도 기반으로 절대평가하고 그 결과를 상대비교하는 방법으로 개별서비스 간 가중치를 산출하는 방안도 함께 제시한다. 연구결과: 생활안전 예방서비스에 대한 서비스 가중치를 도출하고, 이를 생활안전 예방서비스의 인공지능 예측모델을 통해 산출된 개별위험지수에 반영하여 종합지수를 산출하였다. 결론: 구현한 모델의 적용을 위하여 생활안전 예방서비스 앱과 플랫폼으로 구성된 테스트 환경을 구축하고, 사용자 시나리오를 바탕으로 기능에 대한 효능을 평가하였다. 이를 통해 본 연구에서 제시된 생활안전지수는 사용자에게 현재 자신의 안전수준을 종합하여 나타냄으로써 안전 위험에 진단과 대응 및 예방 골든타임을 지원하는 것으로 기대된다.

• 한국측량학회지
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• 제35권2호
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• pp.113-122
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• 2017

Natural disasters caused by climate change are increasing globally. There are few studies on the quantitative analysis methods for predicting damages in the island area due to sea level rise. Therefore, it is necessary to study the damage prediction analysis method using the GIS which can quantitatively analyze. In this paper, we analyze the cause and status of sea level rise, quantify the vulnerability index, establish an integrated terrestrial modeling method of the ocean and land, and establish a method of analyzing the damage area and damage scale due to sea level rise using GIS and the method of making the damage prediction figure was studied. In order to extract the other affected areas to sea level rise are apart of the terrain model is generated by one requires a terrain modeling of target areas are offshore and vertical reference system differences in land, found the need for correction by a tidal observations and geoid model there was. Grading of terrain, coastline erosion rate, coastal slope, sea level rise rate, and even average by vulnerable factors due to sea level rise indicates that quantitative damage prediction is possible due to sea level rise in the island area. In the case of vulnerable areas extracted by GIS, residential areas and living areas are concentrated on the coastal area due to the nature of the book area, and field survey shows that coastal changes and erosion are caused by sea level rise or tsunami.

• 한국기후변화학회지
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• 제6권4호
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• Korean Chemical Engineering Research
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• 제57권5호
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• pp.637-651
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• 2019

정유 또는 석유화학플랜트에서 폭발 등과 중대사고가 발생할 때 심각한 인명 및 재산피해를 야기시켜 보험시장에 큰 영향을 끼쳐왔다. 일반적으로 정유, 석유화학공장 등 장치산업에서 이러한 사고 발생시 국립과학수사연구원, 사고원인 조사자, 손해보험사 손해사정사 등이 조사하여 손해 및 사고와 가장 인접한 근인 위주로 사고원인을 도출하고 있다. 반면 실제 중대사고로 이어지기까지 문제 및 결함으로 작용한 여러 중대사고 전조신호에 대한 근본원인분석을 실시하여 예방대책을 수립하는 것이 중요하나 그 동안이에 대한 것이 미흡하였다. 이에 본 연구에서는 전 세계에서 발생된 중대사고 사례에 대하여 근본원인분석 방법과 스위스치즈모델 원리를 활용한 기여요소분석법 등을 통하여 도출하였던 미국 화학공정안전센터의 중대사고 전조신호 자체평가 도구의 전조신호 판단기준 항목을 우선적으로 고찰하였다. 여기에 실제 정유 및 석유화학플랜트 내 중대사고 전조신호가 해외재보험사 Loss control engineer 등 Auditor 들에게 어떠한 식으로 권고되어 왔는지 확인하고자 지난 17년간 Loss Control Engineer가 Risk Survey 이후 도출하였던 안전권고사항 약 614개를 분석하였다. 최종적으로 이를 중대사고 전조신호 평가지표로 개발이 용이하도록 정유 및 석유화학플랜트에서의 중대사고 전조신호 판단기준을 유형별로 그룹화한 후 상위 및 하위 항목으로 구분하였다. 또한, 정유 및 석유화학공장 관련 전문가(40명)에게 설문 실시 및 AHP기법을 적용하여 각 항목별 가중치(중요도)를 도출하여 최종 전조신호 판단기준과 항목별 가중치가 적용된 '정유 및 석유화학플랜트에서의 중대사고 전조신호 평가지표'를 개발하였다. 그리고 개발한 지표를 8개의 정유 및 석유화학플랜트에 적용하여 분석하였다. 본 연구에서 개발된 평가지표는 정유 및 석유화학공장 등 장치산업에서의 중대사고 예방을 위하여 추적관리 되어야 할 전조신호 항목 및 요소가 무엇인지 인식하고 취약수준을 평가하는데 도움이 될 수 있고, 관련 사업장 자체 관계자뿐만 아니라 외부 auditor들에게 유용하게 활용되리라 판단된다.

• 한국측량학회지
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• 제37권5호
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• pp.303-313
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• 2019

After a disaster happens in urban areas, many people need support for a quick evacuation. This work aims to develop a method for the calculation of the most feasible evacuation route inside buildings. In the methodology we simplify the geometry of the structural and non structural elements from the BIM (Building Information Modeling) to store them in a spatial database which follows standards to support vector data. Then, we apply the multicriteria analysis with the allocation of prioritization values and weight factors validated through the AHP (Analytic Hierarchy Process), in order to obtain the Importance Index S(n) of the elements. The criteria consider security conditions and distribution of the building's facilities. The S(n) is included as additional heuristic data for the calculation of the evacuation route through an algorithm developed as a variant of the $A^*$ pathfinding, The experimental results in the simulation of evacuation scenarios for vulnerable people in healthy physical conditions and for the elderly group, shown that the conditions about the wide of routes, restricted areas, vulnerable elements, floor roughness and location of facilities in the building applied in the multicriteria analysis has a high influence on the processing of the developed variant of $A^*$ algorithm. The criteria modify the evacuation route, because they considers as the most feasible route, the safest instead of the shortest, for the simulation of evacuation scenarios for people in healthy physical conditions. Likewise, they consider the route with the location of facilities for the movement of the elderly like the most feasible in the simulation of evacuation route for the transit of the elderly group. These results are important for the assessment of the decision makers to select between the shortest or safest route like the feasible for search and rescue activities.

• Advances in concrete construction
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• 제14권3호
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• pp.153-167
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• 2022

The interaction between blast load and structures, as well as the interaction among structural members may well affect the structural response and damages. Therefore, it is necessary to analyse more realistic reinforced concrete structures in order to gain an extensive knowledge on the possible structural response under blast load effect. Among all the civilian structures, columns are considered to be the most vulnerable to terrorist threat and hence detailed investigation in the dynamic response of these structures is essential. Therefore, current research examines the effect of blast loads on the reinforced concrete columns via development of Pressure- Impulse (P-I) diagrams. In the finite element analysis, the level of damage on each of the aforementioned RC column will be assessed and the response of the RC columns when subjected to explosive loads will also be identified. Numerical models carried out using LS-DYNA were compared with experimental results. It was shown that the model yields a reliable prediction of damage on all RC columns. Validation study is conducted based on the experimental test to investigate the accuracy of finite element models to represent the behaviour of the models. The blast load application in the current research is determined based on the Lagrangian approach. To develop the designated P-I curves, damage assessment criteria are used based on the residual capacity of column. Intensive investigations are implemented to assess the effect of column dimension, concrete and steel properties and reinforcement ratio on the P-I diagram of RC columns. The produced P-I models can be applied by designers to predict the damage of new columns and to assess existing columns subjected to different blast load conditions.

• 한국지형학회지
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• 제27권3호
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.