• 제목/요약/키워드: disaster prediction

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복합재난 예측 모형 설계를 위한 공통 입출력 파라미터 도출 연구 (A study of extract common I/O parameter for design of complex disaster prediction model)

  • 이병훈;이병진;오승희;이용태;김경석
    • 한국위성정보통신학회논문지
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    • 제12권4호
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    • pp.34-41
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    • 2017
  • 본 논문은 기존에 개발된 자연재난 예측 모형과 사회재난 예측 모형을 통합한 복합재난 예측 모형을 구성하기 위하여 기존 예측 모형들의 입출력 파라미터를 분석하였다. 복합 재난 예측 모형은 단일 재난이 아닌 다수의 재난이 복합적으로 일어나는 상황을 나타낸다. 이러한 복합재난은 주로 자연재난으로 인해 발생한 사회재난으로 연계되는 경우가 주를 이루기 때문에 자연재난과 사회재난 예측 모형의 연결방안에 대해 연구를 진행하였다. 기존에 개발된 예측 모형 중 재난 유형별로 몇 가지 예측 모형을 기준으로 분석하였으며 재난 유형별로 공통적으로 적용되는 입출력 파라미터가 도출되었다. 본 논문은 향 후 진행되어질 복합재난 예측 모형 구축을 위한 연구에 도움이 될 것이라 생각한다.

재난예측 기술 개발 및 서비스 제공 동향 (Trends in Disaster Prediction Technology Development and Service Delivery)

  • 박소영;홍상기;이강복
    • 전자통신동향분석
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    • 제35권1호
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    • pp.80-88
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    • 2020
  • This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

PREDICTION OF TOKAI EARTHQUAKE DISASTER DAMAGE IN HAMAMATSU CITY AND THE COMPARISON TO THE PREDICTION REPORT OF SHIZUOKA PREFECTURE GOVERNMENT USING GIS

  • Iwasaki, Kazutaka;Komiyaka, Tsukasa
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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    • pp.321-324
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    • 2007
  • It is commonly believed that a gigantic earthquake (Tokai Earthquake) could occur in Shizuoka Prefecture in the near future. The Shizuoka Prefecture Government made the prediction report of Tokai Earthquake disaster damage. But this report does not pay attention to the ground conditions. The authors make a prediction map using GIS of Tokai Earthquake disaster damage in Asada-cho and Hirosawa Ni-chome in the central Hamamatsu City and revealed the location of dangerous houses and dangerous points in road networks in each town. These information could be useful when people try to find escape routes in an earthquake.

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실외 센서네트워크 기반 재해방지 시스템을 위한 위험지역 예측기법 (Dangerous Area Prediction Technique for Preventing Disaster based on Outside Sensor Network)

  • 정영진;김학철;류근호
    • 정보처리학회논문지D
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    • 제13D권6호
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    • pp.775-788
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    • 2006
  • 무선 통신 기술의 발달과 무선기기의 소형화, 그리고 환경에 대한 감지와 제어를 수행하는 센서 네트워크 기술의 확산으로 환경오염, 터널 및 건축물의 붕괴, 홍수, 태풍, 지진 등의 재난에 대비하기 위한 재해 감시 시스템에 대한 연구가 활발히 진행되고 있다. 재해 감시 시스템은 원격지의 상태 정보를 검출하고, 재해 상황을 판단하기 위한 규칙 처리 과정 후, 인식된 상황에 따라 방재작업을 지원한다. 그러나 기존의 모니터링 시스템들은 주로 현재 데이터를 위주로 간단한 집계함수 및 연산자만을 지원하기 때문에, 재해를 미리 예측하고 방재하기에는 부족한 점이 있다. 따라서 이 논문에서는 실외 센서 네트워크 및 공간 정보를 고려하여 재해 위험 지역을 미리 예측하는 재해 방지 시스템을 설계, 구현한다. 제시된 위험 지역 예측 기법은 현재 센서 데이터를 토대로 시간에 대한 공간 정보의 변화를 고려하여 미래에 재해 위험이 있는 지역을 추정하여 제시한다. 이로 인해 재해를 미리 예측하고 방비할 수 있게 되고, 재해 피해 감소 및 복구비용을 줄일 수 있다. 제시된 재해 방지 시스템과 위험 지역 예측 기법은 센서네트워크를 기반으로 하는 다양한 재해 방지 시스템에 활용되어 재해 예방 및 피해 감소에 많은 도움을 줄 수 있다.

APPLICATION OF 3D TERRAIN MODEL FOR INDUSTRY DISASTER ASSESSMENT

  • Kim, Hyung-Seok;Cho, Hyoung-Ki;Chang, Eun-Mi;Kim, In-Hyun;Kim, In-Won
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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    • pp.3-5
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    • 2008
  • An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmental description of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapour Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapour Explosion), Fireball and so on, among them, we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

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공간 예측 모델을 이용한 산사태 재해의 인명 위험평가 (Life Risk Assessment of Landslide Disaster Using Spatial Prediction Model)

  • 장동호
    • 환경영향평가
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    • 제15권6호
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    • pp.373-383
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    • 2006
  • The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

농촌지역 돌발재해 피해 경감을 위한 USN기반 통합예경보시스템 (ANSIM)의 개발 (Development of an Integrated Forecasting and Warning System for Abrupt Natural Disaster using rainfall prediction data and Ubiquitous Sensor Network(USN))

  • 배승종;배원길;배연정;김성필;김수진;서일환;서승원
    • 농촌계획
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    • 제21권3호
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    • pp.171-179
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    • 2015
  • The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.

태풍에 따른 지역별 건물피해액 예측모델 개발 기초연구 (A Basic Study on Reginal Prediction Model for Building Damage Costs acrroding to Hurricane)

  • 김부영;양성필;김상호;조한병;손기영
    • 한국건축시공학회:학술대회논문집
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    • 한국건축시공학회 2015년도 춘계 학술논문 발표대회
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    • pp.253-254
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    • 2015
  • Currently, according to the climate change, the damages due to the hurricane is more increased than before. In this respect, several countries have been conducted the studies regarding the damage prediction model of buildings to minimize the damages from natural disaster. As hurricane is the complex disaster including a strong wind and heavy rain, to predict the damage of hurricane, various factors has to be considered. However, mostly research has been conducted to consider only hurricane properties. Therefore, the objective of this study is to develop the regression model for predicting damages of buildings considering geography, socio-economy, construction environment and hurricane information. In the future, this study can be utilized to developing damage prediction model for building from hurricane in South Korea.

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