• Title/Summary/Keyword: 도시재난

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The Change in the Influence of Environmental Factors on Depression by the COVID-19 Pandemic (COVID-19 팬데믹 직전과 직후 우울감에 영향을 미치는 지역환경 요인의 변화 연구)

  • Kim, EunJi;Jung, Suyoung;Jun, Hee-Jung
    • Journal of the Korean Regional Science Association
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    • v.40 no.1
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    • pp.19-35
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    • 2024
  • This research aims to compare and analyze changes in local physical environmental factors affecting mental health before and after the occurrence of COVID-19. The research question is: "Did the influence of environmental factors affecting mental health change after the emergence of the COVID-19 pandemic?" To examine the research question, the study considered the year 2019, right before COVID-19, and the year 2020, the year when COVID-19 occurred, as the temporal scope of the research. For the empirical analysis, we used multilevel logistic analysis was conducted using data from the Community Health Survey for each year and the National Statistical Office (KOSIS). The results can be summarized as follows: After the occurrence of COVID-19, physical environmental factors showed stronger associations with mental health compared to before the emergence of COVID-19. Specifically, it was found that park area per thousand people and the proportion of pedestrian-only road areas were further associated with a decrease in depression. Based on these findings, this study suggests the need for improving and constructing the physical environment in local communities for preventing mental health issues during disaster situations such as COVID-19.

Earthquake Response Analysis of Cylindrical Liquid-Storage Tanks Considering Nonlinear Fluid-Structure Soil Interactions (비선형 유체-구조물-지반 상호작용 고려한 원통형 액체저장탱크의 지진응답해석)

  • Jin Ho Lee;Jeong-Rae Cho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.37 no.2
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    • pp.133-141
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    • 2024
  • Considering fluid-structure-soil interactions, a finite-element model for a liquid-storage tank is presented and the nonlinear earthquake response analysis is formulated. The tank structure is modeled considering shell elements with geometric and material nonlinearities. The fluid is represented by acoustic elements and combined with the structure using interface elements. To consider the soil-structure interactions, the near- and far-field regions of soil are modeled with solid elements and perfectly matched discrete layers, respectively. This approach is applied to the seismic fragility analysis of a 200,000 kL liquid-storage tank. The fragility curve is observed to be influenced by the amplification and filtering of rock outcrop motions at the site when the soil-structure interactions are considered.

Enhanced ACGAN based on Progressive Step Training and Weight Transfer

  • Jinmo Byeon;Inshil Doh;Dana Yang
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.3
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    • pp.11-20
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    • 2024
  • Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

An Investigation of the Factors Affecting Satisfaction with Cell Broadcast Service(CBS) -Focusing on Users in Incheon- (긴급재난문자 만족도에 영향을 미치는 요인 규명 -인천광역시 서비스 대상자를 중심으로-)

  • Park, Keon-Oh;Park, Jae-Young
    • Journal of Environmental Science International
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    • v.33 no.3
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    • pp.193-203
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    • 2024
  • This study aims to determine the factors affecting the level of satisfaction with the Cell Broadcast Service (CBS) among citizens in Incheon. Partial least squares (PLS) regression, instead of multiple regression, was used for the analysis because it can solve multicollinearity and sample size issues. The analysis results are as follows: The factor with the greatest effect on satisfaction with CBS among Incheon citizens, was the elimination of redundancies (VIP=1.185). Therefore, local governments, government agencies, and public organizations must coordinate their ideas and collectively create guidelines to eliminate redundancies. The second most influential factor was the expansion in the broadcast medium from legal, institutional, and policy aspects (VIP=1.087). This is because differences in generation, age, gender, and personal characteristics were not considered. Therefore, it is necessary to devise a customized messaging tool through the expansion of broadcast media. The broadcast criteria of the legal, institutional, and policy perspectives comprised the third most influential factor, with a high VIP value of 1.053. Consequently, it is essential to devise a plan to avoid distributing unnecessary cell broadcast services, by establishing criteria for areas and sections, time, and the direct and indirect impact zones of a disaster. In the future, this study could be used as base data to develop policies, guidelines, and response measures for Incheon CBS. Given the lack of research on the diverse characteristics of each social class and the city traits of each region, and a lack of concrete empirical research on each factor, continuous and in-depth studies are required in the future.

Study on the Fire Risk Prediction Assessment due to Deterioration contact of combustible cables in Underground Common Utility Tunnels (지하공동구내 가연성케이블의 열화접촉으로 인한 화재위험성 예측평가)

  • Ko, Jaesun
    • Journal of the Society of Disaster Information
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    • v.11 no.1
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    • pp.135-147
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    • 2015
  • Recent underground common utility tunnels are underground facilities for jointly accommodating more than 2 kinds of air-conditioning and heating facilities, vacuum dust collector, information processing cables as well as electricity, telecommunications, waterworks, city gas, sewerage system required when citizens live their daily lives and facilities responsible for the central function of the country but it is difficult to cope with fire accidents quickly and hard to enter into common utility tunnels to extinguish a fire due to toxic gases and smoke generated when various cables are burnt. Thus, in the event of a fire, not only the nerve center of the country is paralyzed such as significant property damage and loss of communication etc. but citizen inconveniences are caused. Therefore, noticing that most fires break out by a short circuit due to electrical works and degradation contact due to combustible cables as the main causes of fires in domestic and foreign common utility tunnels fire cases that have occurred so far, the purpose of this paper is to scientifically analyze the behavior of a fire by producing the model of actual common utility tunnels and reproducing the fire. A fire experiment was conducted in a state that line type fixed temperature detector, fire door, connection deluge set and ventilation equipment are installed in underground common utility tunnels and transmission power distribution cables are coated with fire proof paints in a certain section and heating pipes are fire proof covered. As a result, in the case of Type II, the maximum temperature was measured as $932^{\circ}C$ and line type fixed temperature detector displayed the fire location exactly in the receiver at a constant temperature. And transmission power distribution cables painted with fire proof paints in a certain section, the case of Type III, were found not to be fire resistant and fire proof covered heating pipes to be fire resistant for about 30 minutes. Also, fire simulation was carried out by entering fire load during a real fire test and as a result, the maximum temperature is $943^{\circ}C$, almost identical with $932^{\circ}C$ during a real fire test. Therefore, it is considered that fire behaviour can be predicted by conducting fire simulation only with common utility tunnels fire load and result values of heat release rate, height of the smoke layer, concentration of O2, CO, CO2 etc. obtained by simulation are determined to be applied as the values during a real fire experiment. In the future, it is expected that more reliable information on domestic underground common utility tunnels fire accidents can be provided and it will contribute to construction and maintenance repair effectively and systematically by analyzing and accumulating experimental data on domestic underground common utility tunnels fire accidents built in this study and fire cases continuously every year and complementing laws and regulations and administration manuals etc.

Derivation of Green Infrastructure Planning Factors for Reducing Particulate Matter - Using Text Mining - (미세먼지 저감을 위한 그린인프라 계획요소 도출 - 텍스트 마이닝을 활용하여 -)

  • Seok, Youngsun;Song, Kihwan;Han, Hyojoo;Lee, Junga
    • Journal of the Korean Institute of Landscape Architecture
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    • v.49 no.5
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    • pp.79-96
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    • 2021
  • Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.

Study on Predicting Changes in Traffic Demand in Surrounding SOCs Due to Road SOC Construction Using Big Data - Centered Around the Connecting Road between Incheon Yeongjong International City and Cheongna International City (3rd Bridge) - (빅데이터를 활용한 도로 SOC건설에 따른 주변 SOC 교통수요 변화 예측 연구 - 인천 영종국제도시~청라국제도시 간 연결도로(제3연륙교)를 중심으로 -)

  • Byoung-Jo Yoon;Sang-Hun Kang;Seong-Jin Kim
    • Journal of the Society of Disaster Information
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    • v.20 no.3
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    • pp.705-713
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    • 2024
  • Purpose: Currently, the only routes that enter Yeongjong Island are Yeongjong Bridge and Incheon Bridge, which are private roads. The purpose of this study is to predict and study changes in transportation demand for new routes and two existing routes according to the plan to open the 3rd Bridge, a new route, in December 2025. Method: The basic data for traffic demand forecast were O/D and NETWORK data from 2021.08, KOTI. In order to examine the reliable impact of Yeongjong Bridge and Incheon Bridge on the opening of the 3rd Bridge, it is necessary to correct the traffic distribution of Yeongjong Island and Incheon International Airport to suit reality, and in this study, the trip distribution by region was corrected and applied using Mobile Big Data. Result: As of 2026, the scheduled year of the opening of the 3rd Bridge, two alternatives, Alternative 1 (2,000 won) and Alternative 2 (4,000 won), were established and future transportation demand analysis was conducted, In the case of Alternative 1, which is similar to the existing private road toll restructuring, the traffic volume of the 3rd Bridge was predicted to be 42,836 out of 199,101 veh/day in the Yeongjong area in 2026, and the traffic volume reduction rate of the existing road was analyzed as 21.5%. Conlclusion: As a result of the review (based on Alternative 1), the proportion of convertted traffic on the 3rd Yanji Bridge was estimated to be 70% of Yeongjong Bridge and 30% of Incheon Bridge, and 21.5% of the predicted traffic reduction on the existing road when the 3rd Yanji Bridge was opened is considered appropriate considering the results of the case review and changes in conditions. It is judged that it is a way to secure the reliability of the prediction of traffic demand because communication big data is used to reflect more realistic traffic distribution when predicting future traffic demand.

The study of heavy rain warning in Gangwon State using threshold rainfall (침수유발 강우량을 이용한 강원특별자치도 호우특보 기준에 관한 연구)

  • Lee, Hyeonjia;Kang, Donghob;Lee, Iksangc;Kim, Byungsikd
    • Journal of Korea Water Resources Association
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    • v.56 no.11
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    • pp.751-764
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    • 2023
  • Gangwon State is centered on the Taebaek Mountains with very different climate characteristics depending on the region, and localized heavy rainfall is a frequent occurrence. Heavy rain disasters have a short duration and high spatial and temporal variability, causing many casualties and property damage. In the last 10 years (2012~2021), the number of heavy rain disasters in Gangwon State was 28, with an average cost of 45.6 billion won. To reduce heavy rain disasters, it is necessary to establish a disaster management plan at the local level. In particular, the current criteria for heavy rain warnings are uniform and do not consider local characteristics. Therefore, this study aims to propose a heavy rainfall warning criteria that considers the threshold rainfall for the advisory areas located in Gangwon State. As a result of analyzing the representative value of threshold rainfall by advisory area, the Mean value was similar to the criteria for issuing a heavy rain warning, and it was selected as the criteria for a heavy rain warning in this study. The rainfall events of Typhoon Mitag in 2019, Typhoons Maysak and Haishen in 2020, and Typhoon Khanun in 2023 were applied as rainfall events to review the criteria for heavy rainfall warnings, as a result of Hit Rate accuracy verification, this study reflects the actual warning well with 72% in Gangneung Plain and 98% in Wonju. The criteria for heavy rain warnings in this study are the same as the crisis warning stages (Attention, Caution, Alert, and Danger), which are considered to be possible for preemptive rain disaster response. The results of this study are expected to complement the uniform decision-making system for responding to heavy rain disasters in the future and can be used as a basis for heavy rain warnings that consider disaster risk by region.

A Study on Precision of 3D Spatial Model of a Highly Dense Urban Area based on Drone Images (드론영상 기반 고밀 도심지의 3차원 공간모형의 정밀도에 관한 연구)

  • Choi, Yeon Woo;Yoon, Hye Won;Choo, Mi Jin;Yoon, Dong Keun
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.40 no.2
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    • pp.69-77
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    • 2022
  • The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

Development of 1ST-Model for 1 hour-heavy rain damage scale prediction based on AI models (1시간 호우피해 규모 예측을 위한 AI 기반의 1ST-모형 개발)

  • Lee, Joonhak;Lee, Haneul;Kang, Narae;Hwang, Seokhwan;Kim, Hung Soo;Kim, Soojun
    • Journal of Korea Water Resources Association
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    • v.56 no.5
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    • pp.311-323
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    • 2023
  • In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.