A Study on the Risk Prediction Technologies Based on the Disaster Data

doi:None

All Issue

2022 Vol.7, Issue 2 Preview Page Next Page

Article

A Study on the Risk Prediction Technologies Based on the Disaster Data 재난 사고 데이터 기반 위험 예측 기술 연구: 이창열^1†
Lee, ChangYeol^1†; ¹동의대학교 컴퓨터공학과 교수

¹Professor, Department of Computer Engineering, Dongeui University

31 August 2022. pp. 131-137

PDF

Abstract

Although we can not control the occurrences of Natural Disaster, we can reduce the occurrences of the social disaster by the safety control and preventions activities. Using Heinrich’s law, we can predict the possible disasters from the news data. In this study, we construct the incident and disaster case DB. AI models such as BERT and MLP, learn the characteristics of the DB. and then predict the risk occurrence possibilities and damage scale from the extracted news data of the news data. We design the operation platform with the DB, AI model and real-time news monitoring from the web.

Keywords

Social disaster

Incident cases

Heinrich’s law

Risk prediction

자연 재난은 발생 자체를 제어할 수 없지만, 사회 재난인 경우에 안전 점검과 예방 활동을 통하여 발생을 감소시킬 수 있다. 사고 사례를 분석을 통하여 예방, 대비 활동을 진행하고 있고, 하인리히 법칙처럼 위험에 대한 전조를 기반으로 발생 재난을 예측하여 예방, 대비를 할 수 있다. 본 연구에서는 사고 데이터의 특성을 AI 모델을 통하여 분석하고 이를 기반으로 뉴스와 같은 실시간 데이터를 모니터링하여 발생 가능한 사고에 대한 예측과 피해 규모를 예측하는 시스템 설계를 제시하였다.

키워드

사회재난

사고 사례

인공지능

하인리히 법칙

위험 예측

References

Kim, M. J. (2008). Heinrich’s law, Ornado, ISBN-9788992060660.
Lee, C. Y., Kim, T. H. (2021). A study of the standard structure for the social disaster and safety incidents data, KOSDI, Vol.17, No.4, pp.817-828.
Lee, C. Y., Kim, T. H., Cha, S. Y. (2011). A study of the extraction Algorithm of the disaster sign data from Web, KOSDI, Vol.7, No.2, pp.139-149.
MOIS (2016). A study of Disaster and Incident Operation Support System, 12, 2016.
NDMI (2018). Development of horizon scanning technology for weak signal of future emerging disaster based on big data, NDMI 11-1741056-000149-01.
NDMI (2020). Preparation of Unstructured Data Collection and Exploration System to Analysis Social and Public Interests in the Disaster and Safety Field, NDMI 11-1741056-000269-01.

Information

Publisher :National Heritage Disaster Prevention Society
Publisher(Ko) :국가유산방재학회
Journal Title :Journal of the Society of Cultural Heritage Disaster Prevention
Journal Title(Ko) :저널국가유산
Volume : 7
No :2
Pages :131-137

[1] Kim, M. J. (2008). Heinrich’s law, Ornado, ISBN-9788992060660.

[2] Lee, C. Y., Kim, T. H. (2021). A study of the standard structure for the social disaster and safety incidents data, KOSDI, Vol.17, No.4, pp.817-828.

[3] Lee, C. Y., Kim, T. H., Cha, S. Y. (2011). A study of the extraction Algorithm of the disaster sign data from Web, KOSDI, Vol.7, No.2, pp.139-149.

[4] MOIS (2016). A study of Disaster and Incident Operation Support System, 12, 2016.

[5] NDMI (2018). Development of horizon scanning technology for weak signal of future emerging disaster based on big data, NDMI 11-1741056-000149-01.

[6] NDMI (2020). Preparation of Unstructured Data Collection and Exploration System to Analysis Social and Public Interests in the Disaster and Safety Field, NDMI 11-1741056-000269-01.

Journal National Heritage ISSN:3022-9200(Print) 3022-9383(Online) 저널 국가유산

All Issue