DOI QR코드

DOI QR Code

Trends in Disaster Prediction Technology Development and Service Delivery

재난예측 기술 개발 및 서비스 제공 동향

  • Published : 2020.02.01

Abstract

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.

Keywords

References

  1. 재난 및 안전관리 기본법, 국가법령정보센터, http://www.law.go.kr/
  2. https://wifire.ucsd.edu/
  3. https://www.cmu.edu/metro21/projects/fire-risk-analysis.html
  4. Madaio, Michael A., "Predictive Modeling of Building Fire Risk: Designing and Evaluating Predictive Models of Fire Risk to Prioritize Property Fire Inspections," Carnegie Mellon University, 2018.
  5. https://github.com/CityofPittsburgh/fire_risk_analysis
  6. https://nctr.pmel.noaa.gov/index.html
  7. https://www.ncdc.noaa.gov/data-access/model-data)
  8. https://www.yna.co.kr/view/AKR20190819071400077
  9. https://news.abs-cbn.com/business/07/10/19/google-aimakes-disaster-alerts-more-precise-targeted
  10. Sella Nevo1 Vova Anisimov et al., "ML for Flood Forecasting at Scale," 32nd Conference on Neural Information Processing Systems (NIPS 2018), December, 2018.
  11. https://news.abs-cbn.com/business/07/10/19/google-aimakes-disaster-alerts-more-precise-targeted
  12. http://www.hani.co.kr/arti/science/science_general/766979.html
  13. Phoebe M. R. DeVries et al., "Deep learning of aftershock patterns following large earthquakes," Nature, 29 August 2018.
  14. "지진 조기 경보 더 빨라지고, 지진 진도정보 지역별로 알려준다!" 기상청 보도자료, 2018.11.28.