• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.177-177
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• 2021

우리나라는 최근 이상기후로 유발되는 여름철 태풍과 집중호우로 인해 매년 심각한 홍수피해가 발생되고 있다. 또한, 도시화에 따른 자산 및 인구의 집중화로 인해 홍수피해 규모도 지속적으로 증가하고 있다. 이와 같은 재난 발생 시 위험도를 저감하기 위해서는 재난에 대한 예방이 선행되어야 한다. 한정된 예산에서 효율적인 재난관리의 일환으로 경제성 분석이 있는데, 이는 경제성이 높은 사업을 우선적으로 순위에 두고 시행하는 것이다. 다차원 홍수피해 산정법(이하 다차원법)은 2004년에 개발된 경제성 평가모형으로서, 예상피해액 산정 결과가 개선법에 비해 정확하다고 평가되고 있다. 하지만 다차원법에 적용되는 토지피복도는 동일한 구역에 있는 군집으로 나누어진 자료로 건물피해액 산정 시 도로면적까지 포함되는 경우가 있어 실제 피해액이 과대평가되는 문제가 있다. 이에 본 연구에서는 도로명주소 전자지도를 활용해 다차원법을 보완하고자 하였다. 여기서 도로명주소 전자지도는 폴리곤 객체 단위로 건물이 구현되는 공간분석 자료로 건물피해액 산정 방법을 개선할 수 있다. 본 연구에서는 경안천 유역의 홍수위험지도를 사용하여 도로명주소 전자지도와 토지피복도를 각각 적용했을 때 나타나는 경제성 분석 결과를 비교분석 하였다. 분석 결과, 기존의 토지피복도를 사용한 피해액 산정결과보다 개선된 공간분석 자료를 적용했을 때 더 정확한 예측피해액 산정결과를 얻을 수 있었다. 본 연구에서는 다차원법의 일반자산 피해항목 중 건물피해 산정방법만을 개선시켰는데, 향후 연구에서 농업지역, 산업지역, 공공시설물 피해 등의 산정방법의 보완이 필요할 것으로 사료된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.144-158
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• 2014

This research presented a conceptual design of damage assessment inventory for efficient response to natural disaster damage of infrastructure close to the river. It consists of classification and categorization of facilities for accomplishing the conceptual design of inventory for damage of infrastructure close to the river. However, there are arising problems of efficient management on disaster, such as poor management of data facilities and constructions which is managed by the different types of government departments. Therefore, this research presented conceptual models of damage assessment inventory on risks of damage infrastructure close to the river using the United states' HAZUS-MH to analyze damage facilities, type of asset classification, classification of domestic facilities and guidelines for computing the value of assets. Conceptual models of inventory this research presented is to be used on the data for damage response on protected inland damage assessment and to increase efficiency for evaluating detailed damage amount of private property by natural disaster and to establish a restoration plan.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.351-351
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• 2021

저수지에 대한 비상대처계획수립은 최근 기상이변 등에 따른 대규모 호우가 빈번히 발생하고 있을 뿐만 아니라 세계 도처에서 대규모 지진 등으로 많은 인명과 재산 피해가 속출함에 따라 지진 및 이상홍수에 대한 저수지의 안정성 평가를 수행하고 저수지 지점에서 발생할 수 있는 천재지변 또는 예상치 못한 대규모 재해에 효율적으로 대처하기 위한 비상상황의 등급 및 위험수준을 체계적으로 판단하고 비상상황 가상 시나리오별 체계적 행동요령 및 대처계획을 수립하여 저수지 붕괴에 따른 대규모 홍수피해 예상지역 주민들의 신속한 대응으로 생명과 재산피해를 최소화하는데 목적이 있다. 현행 한국농어촌공사 및 지자체에서 수립하고 있는 30만 톤 이상 저수지에 대한 1차원 모형 기반의 EAP수립은 침수구역을 산정할 때 수치지도에 의한 단일 침수심 분석으로 실제 침수구역과는 많은 오류가 나타난다. 이는 침수구역 부정확에 따른 피해복구액 산정이 과다로 책정될 수 있고, 마지막으로 가장 중요한 비상대처계획 수립에 막대한 영향을 미친다. 이에 본 연구는 댐 붕괴에 대응하기 위한 EAP 수립 시 기본이 되는 홍수범람해석을 수행하고 1차원 및 2차원 모형의 결과검토를 통해 보다 효과적인 비상대처계획의 수립을 위한 방안을 제시하고자 하였다. 이를 위해 경천저수지 유역을 대상으로 가능최대강수량 조건 하에서 가능최대홍수량을 산정하고 DAMBRK 모형을 이용하여 댐 붕괴 모의를 위한 시나리오 구성 및 모의를 수행하였다. 이후 댐 붕괴 모의결과를 이용하여 WMS(Watershed Modeling System) 모형을 이용한 1차원 홍수범람해석과 FLUMEN(FLUvial Modeling ENgine) 모형을 이용한 2차원 홍수범람해석에 적용 후 각 결과를 비교·검토하였다.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.97-97
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• 2023

최근 재해분야에서 인공신경망(ANN), 기계학습(ML), 딥러닝(DL) 등 AI 기술이 활용성이 점차 증가하고 있으며, 센싱정보와 연계한 시설물 안전관리, 원격탐사와 연계한 재해감시(녹조, 산사태, 산불 등), 수문시계열(수위, 유량 등) 예측, 레이더·위성강수 자료의 보정과 예측, 상하수도 관망누수예측 등 다양한 분야에서 AI 기술이 적용되고 그 활용성이 검증된 바 있다. 본 연구에서는 ML, DL, 물리기반신경망(Pysics-informed Neural Networks, PINNs)을 이용한 다양한 재해분석 사례를 소개하고, 그 활용성과 한계에 대해서 논의하고자 한다. 주요사례로는 (1) SAR영상과 기계학습을 이용한 재해피해지역(울진 산불) 감지, (2) 국가 디지털 정보를 이용한 산사태 위험지역 판별(인제 산사태) (3) 기계학습 및 딥러닝 기법을 이용한 위성강수 자료의 보정·예측 및 유출해석, (4) 수리해석을 위한 수치해석분야에서의 PINNs의 적용성(1차원 Saint-Venant 식 해석) 평가 연구결과를 공유한다. 특히, 자료의 입·출력 자료만으로 학습된 인공신경망 모형 대신 지배방정식(물리방정식)을 만족하도록 강제한 PINNs의 경우, 인공신경망 모형보다 우수한 모의능력을 보여주었으며, 향후 복잡한 수리모델링 등 수치해석분야에서 그 활용가능성이 매우 높을 것으로 판단된다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.299-309
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• 2006

In recent years, a large natural disasters have occurred due to worldwide abnormal weather and the amount of damage has been increased more resulting from high density population and a large-sized buildings of the urbanized area. In this study. we estimate the flooded area according to rainfall probability intensify and sea level in Woreong dong, Masan occurred flood damages by typhoon Maemi using SWMM, a dynamic rainfall-runoff simulation model in urban area, and then analyze the damage of flood expected area through connecting with GIS database. In result, we can predict accurately expected area of inundation according to the rainfall intensity and sea level rise through dividing the study area into sub-area and estimating a flooded area and height using SWMM. We provide also the shelter information available for urban planning and flood risk estimation by landuse in expected flood area. Further research for hazard management system construction linked with web or wireless communication technology expects to increase its application.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.88-100
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• 2009

An inundation analysis was performed on Hwapocheon, one of the tributaries of Nakdong River, which was inundated by heavy rain in August, 2002 with overtopping and levee break. The results of the developed model, 2D diffusion wave inundation analysis model, was compared with inundation trace map as well as inundation depth in terms of time and maximum inundated area calculated from FLUMEN model for the assessment of model applicability. The results from the developed model showed high fitness of 88.61% in comparison with observed data. Also maximum inundated area and spatial distribution of inundation zone were also found to be consistent with the results of FLUMEN model. Therefore, inundation zone and maximum inundation area calculated over a period of time by adopting 2D diffusion wave inundation analysis model can be used as a database for identifying high risk areas of inundation and establishing flood damage reduction measures.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.518-526
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• 2018

The purpose of this study is to analyze the effect of social trust on risk perception. In other words, I tried to analyze empirically how the generalized trust about other people they have based on subjective perception of Seoul citizens affects the perception of risk factors. First, the risk factors that Seoul citizens face in everyday life are classified into five categories: natural disaster, technical disaster risk, economic risk, social disintegration risk and health risk. Then, the influence of social trust on each of these risk perception was analyzed by multiple regression model. The results show that social trust has a statistically significant negative impact on all types of risk perception. These results imply that social trust makes low-risk assessments of various risk factors around people. The implication of this study is that the responsibility for risk is given to the central and local governments in the modern risk society. In order to prevent effective risk, it is necessary to make efforts to promote social trust through various activities together with efforts to prevent the spread of unfounded risk will be. And trust among people also promotes cooperation in coping with risks, so it is necessary to promote communication and mutual understanding that can build trust among people in their daily life.

• Journal of Korean Society of Disaster and Security
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• v.17 no.1
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• pp.1-8
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• 2024

In Korea, mountainous areas cover 60% of the land, leading to increased factors such as concentrated heavy rainfall and typhoons, which can result in debris flow and landslide. Despite the high risk of disasters like landslides and debris flow, there has been a tendency in most regions to focus more on post-damage recovery rather than preventing damage. Therefore, in this study, precise topographic data was constructed by conducting on-site surveys and drone measurements in areas where debris flow actually occurred, to analyze the risk zones for such events. The numerical analysis program RAMMS model was utilized to perform debris flow analysis on the areas prone to debris flow, and the actual distribution of debris flow was compared and analyzed to evaluate the applicability of the model. As a result, the debris flow generation area calculated by the RAMMS model was found to be 18% larger than the actual area, and the travel distance was estimated to be 10% smaller. However, the simulated shape of debris flow generation and the path of movement calculated by the model closely resembled the actual data. In the future, we aim to conduct additional research, including model verification suitable for domestic conditions and the selection of areas for damage prediction through debris flow analysis in unmeasured watersheds.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.