AI and Machine Learning for Risk Management
Design and Implementation of a Multi-Disaster Risk Assessment Database System in South Korea
To cope with natural disasters and their impact, various types of risk assessment tools have been developed to assess hazards from a scientific point of view as well as the socioeconomic impacts of a hazardous event. Because most of the assessment tools have been developed using local and regional characteristics, there are limitations when applying special cases consisting of various disaster-related datasets in Korea. This study presents a scalable design method and implementation of a database system which can be easily employed in Korean loss estimation due to natural hazards. The proposed database system will be of great help in predicting the disaster scale and degree of damage, in supporting decision-making to minimize damage, and could serve as a data platform of disaster risk reduction and prevention.
한국형 재난 손실 추정 시스템 및 데이터베이스 구축 & 의사 결정 지원시스템 개발
Open Data Knowledge Platform for Disaster Risk Reduction and Management
An open data knowledge platform, which is a knowledge sharing platform for disaster data collection, management, distribution, and analysis, designed for use in interdisciplinary studies in the field of disaster risk reduction and management, is presented. The proposed system can play an important role as a knowledge hub for supporting the development of disaster response technology as well as for risk mitigation and disaster response, disaster recovery, and risk assessment studies.
재난 위험 감소 및 관리 분야의 최적 설계된 재난 데이 터 수집, 관리, 배포 및 분석을 위한 지식 공유 플랫폼 구축 연구
Design and Implementation of Big Data Analytics Framework for Disaster Risk Assessment
This study proposes a big data based risk analysis framework to analyze more comprehensive disaster risk and vulnerability. We introduce a distributed and parallel framework that allows large volumes of data to be processed in a short time by using open-source disaster risk assessment tool. A performance analysis of the proposed system presents that it achieves a more faster processing time than that of the existing system and it will be possible to respond promptly to precise prediction and contribute to providing guideline to disaster countermeasures.
빅데이터 처리 및 분산시스템 기반 재난 재해 위험도 분석 프레임워크 설계