Development of Disaster Situation Specific Tailored Weather Emergency Information Alert System

doi:10.15683/kosdi.2023.3.31.069

All Issue

2023 Vol.19, Issue 1 Preview Page Next Page

Original Article

Development of Disaster Situation Specific Tailored Weather Emergency Information Alert System 재난 상황별 맞춤형 기상긴급정보 전달 시스템 개발: 김용욱¹*ㆍ권기봉²ㆍ이병윤³
Yong-Yook Kim¹*, Ki-Bong Kwon², Byung-Yun Lee³; ¹Principal Researcher, Climate Technology Team, KIT Valley Inc., Seoul, Republic of Korea
²Research Fellow, Research Planning Division, KIT Valley Inc., Seoul, Republic of Korea
³Principal Researcher, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea

31 March 2023. pp. 69-75

PDF

Abstract

Purpose: The risk of disaster from extreme weather events is increasing due to the increase in occurrence and the strength of heavy rains and storms from continued climate change. To reduce these risks, emergency weather information customized for the characteristics of the information users and related circumstances should be provided. Method: A first-stage emergency weather information delivery system has been developed to provide weather information to the disaster-risk area residents and the disaster response personnel. Novel methods to apply artificial intelligence to identify emergencies have been studied. The relationship between special weather reports from meteorological administration and disaster-related news articles has been analyzed to identify the significance of a pilot study using text analytic artificial intelligence. Result: The basis to identify the significance of the relations between disaster-related articles and special weather reports has been established and the possibility of the development of a real-world applicable system based on a broader analysis of data has been suggested. Conclusion: Through direct alert delivery of weather emergency alerts, a weather emergency alert system is expected to reduce the risk of damage from extreme weather situations.

Keywords

Weather Emergency Information

Disaster Emergency Alert

Artificial Intelligence

Text Analytics

Special Weather Report

연구목적: 지속되는 기후변화에 의한 풍수해 등의 발생 빈도와 강도가 증가하고 있어 극한 기상현상이 지역 특성 및 상황에 따라 재난으로 이어지는 위험성이 높아지고 있어 기상 관련 긴급정보를 사용자 및 사용자 환경에 맞추어 신속하게 제공할 필요가 있다. 연구방법: 기상 재난에 의한 피해 위험 지역 거주 주민과 재난 현장에서 재난에 대응하는 방재 관계기관 등 특정 사용자의 요구에 특화된 맞춤형 기상긴급정보를 전달하기 위한 기상긴급정보전달시스템의 1단계 시스템이 개발되고 인공지능을 활용한 긴급성 식별 방안이 연구되었다. 시범연구로 극한 기상에 의한 재난 뉴스기사를 분석하고 심각성을 식별하여 관련된 기상 특보와 연계하는 방안을 제안하고자 하였다. 연구결과: 1단계 기상긴급정보 전달시스템이 개발되었고 보다 광범위한 자료 분석을 통해 유용한 정보를 추가할 수 있는 방안이 제시되었다. 결론: 기상긴급정보의 직접적이며 신속한 제공을 통해 극한기상에 의한 재난 피해를 줄 일 수 있을 것으로 기대된다.

키워드

기상긴급정보

재난안전긴급알림

인공지능

텍스트 분석

기상특보

References

IPCC Working Group II (2022) Climate Change 2022: Impacts, Adaptation and Vulnerability. Working Group II Contribution to the IPCC Sixth Assessment Report, Cambridge University Press, UK.
Kim, Y., Kim, B., Ko, G., Choi, M., Song, H., Kim, G., Yoo, S., Lim, J., Bok, K., Yoo, J. (2017). "Design and implementation of a flood disaster safety system using realtime weather big data." The Journal of the Korea Contents Association, Vol. 17, No.1, pp. 351-362. 10.5392/JKCA.2017.17.01.351
Ko, J. (2015). "A study on obtaining feedback function of disaster information management using information & communication technology." Journal of the Korea Society of Disaster Information, Vol.11, No.1, pp. 73-88. 10.15683/kosdi.2015.11.1.73
Korean Sentiment Analysis Corpus (2022). Korean Sentiment Lexicon. http://word.snu.ac.kr/kosac/lexicon.php.
Park, S.-H., Kim, H.-J., (2020). "Design of artificial intelligence water level prediction system for prediction of river flood." Journal of the Korea Institute of Information and Communication Engineering, Vol. 24, No. 2, pp.198-203.
Park, S.-M., Na, C.-W., Choi, M.-S., Lee, D.-H., On, B.-W. (2018). "KNU Korean sentiment lexicon: Bi-LSTM-based method for building a Korean sentiment lexicon." Journal of Intelligence and Information Systems, Vol. 24, No. 4, pp. 219-240.
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., Arribas, A., Mohamed, S. (2021). "Skilful precipitation nowcasting using deep generative models of radar." Nature, Vol. 597, No. 7878, pp. 672-677. 10.1038/s41586-021-03854-z 34588668 PMC8481123
Yu, S.-Y., Lee, S.-J., Kim, S.-W., Choi, E.-K., Lee, K.-H., Choi, B.-H. (2015). "Development of a flood loss estimation tool within GIS software." Journal of the Korea Society of Disaster Information, Vol.11, No.3, pp. 436-445. 10.15683/kosdi.2015.11.3.436

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 19
No :1
Pages :69-75
DOI :https://doi.org/10.15683/kosdi.2023.3.31.069

[1] IPCC Working Group II (2022) Climate Change 2022: Impacts, Adaptation and Vulnerability. Working Group II Contribution to the IPCC Sixth Assessment Report, Cambridge University Press, UK.

[2] Kim, Y., Kim, B., Ko, G., Choi, M., Song, H., Kim, G., Yoo, S., Lim, J., Bok, K., Yoo, J. (2017). "Design and implementation of a flood disaster safety system using realtime weather big data." The Journal of the Korea Contents Association, Vol. 17, No.1, pp. 351-362. 10.5392/JKCA.2017.17.01.351

[3] Ko, J. (2015). "A study on obtaining feedback function of disaster information management using information & communication technology." Journal of the Korea Society of Disaster Information, Vol.11, No.1, pp. 73-88. 10.15683/kosdi.2015.11.1.73

[4] Korean Sentiment Analysis Corpus (2022). Korean Sentiment Lexicon. http://word.snu.ac.kr/kosac/lexicon.php.

[5] Park, S.-H., Kim, H.-J., (2020). "Design of artificial intelligence water level prediction system for prediction of river flood." Journal of the Korea Institute of Information and Communication Engineering, Vol. 24, No. 2, pp.198-203.

[6] Park, S.-M., Na, C.-W., Choi, M.-S., Lee, D.-H., On, B.-W. (2018). "KNU Korean sentiment lexicon: Bi-LSTM-based method for building a Korean sentiment lexicon." Journal of Intelligence and Information Systems, Vol. 24, No. 4, pp. 219-240.

[7] Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., Arribas, A., Mohamed, S. (2021). "Skilful precipitation nowcasting using deep generative models of radar." Nature, Vol. 597, No. 7878, pp. 672-677. 10.1038/s41586-021-03854-z 34588668 PMC8481123

[8] Yu, S.-Y., Lee, S.-J., Kim, S.-W., Choi, E.-K., Lee, K.-H., Choi, B.-H. (2015). "Development of a flood loss estimation tool within GIS software." Journal of the Korea Society of Disaster Information, Vol.11, No.3, pp. 436-445. 10.15683/kosdi.2015.11.3.436

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

All Issue