한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제19권2호
  • /
  • Pages.668-675
  • /
  • 2018
  • /
  • 1975-4701(pISSN)
  • /
  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
권호 표지
DOI QR코드

DOI QR Code

재해연보기반 남해연안지역 풍랑피해 예측함수 개발

Development of the Wind Wave Damage Predicting Functions in southern sea based on Annual Disaster Reports

  • 추태호 (부산대학교 사회환경시스템공학과) ;
  • 김영식 (부산대학교 사회환경시스템공학과) ;
  • 심상보 (부산대학교 사회환경시스템공학과) ;
  • 손종근 (부산대학교 사회환경시스템공학과)
  • 투고 : 2017.11.23
  • 심사 : 2018.02.02
  • 발행 : 2018.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

전 세계적으로 도시화와 산업화의 발달은 많은 양의 전력을 필요로 하였다. 그리하여 연안 지역에 원자력 발전소를 비롯한 주요 사회기반시설의 건설이 가속화되었다. 또한 지구 온난화와 이상 기후 현상에 의해 자연 재해의 강도는 증가하고 있다. 자연 재해는 발생 지점과 규모를 예측하기 어렵고, 인명 피해와 재산 피해에 영향을 주고 있다. 이러한 문제로 인하여 연안 지역의 피해 예측과 재해 규모의 산정은 중요한 문제가 되었다. 그리하여 본 연구에서는 예측 가능한 기상 자료를 바탕으로 풍랑 피해의 피해액을 예측하고 예측한 결과를 바탕으로 풍랑 피해에 대하여 사전 대비 차원의 재난 관리가 가능할 것이라 판단된다. 본 연구에서는 재해 통계 자료가 부족한 시 군 구는 인접한 기상 관측소의 자료를 활용하는 지역은 군집분석을 활용하였다. 예측 가능한 기상자료와 지역 등급을 반영하였고, 재해 통계를 기반으로 남해연안지역의 풍랑 피해 예측함수를 개발 하였고, 검증 작업으로는 NRMSE를 활용하였다. 그 결과 NRMSE는 1.61%에서 21.73%로 분석되었다.

The continuing urbanization and industrialization around the world has required a large amount of power. Therefore, construction of major infrastructure, including nuclear power plants in coastal areas, has accelerated. In addition, the intensity of natural disasters is increasing due to global warming and abnormal climate phenomena. Natural disasters are difficult to predict in terms of occurrence, location, and scale, resulting in human casualties and property damage. For these reasons, the disaster scale and damage estimation in coastal areas have become important issues. The present study examined the predictable weather data and regional ratings and developed estimating functions for wind wave damage based on the disaster statistics in the southern areas. The results of the present study are expected to help disaster management in advance of the wind wave damage. The NRMSE was used for verification. The accuracy of the NRMSE results ranged from 1.61% to 21.73%.

키워드

참고문헌

  1. Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.), Climate Change 2014: Synthesis Report, 151 pp, (IPCC, Geneva, Switzerland), 2014
  2. Y. S. Cho, Facilities Safety, Journal of Korea Infrastructure Safety Corporation, vol. 38, pp. 9-21, 2011.
  3. J. S. Lee, Development of Regional Flood Damage Functions for Public Facilities Based on Disaster Statistics and Impact Assessment of Climate Change, Doctoral thesis, Inha Univ Graduate School, 2017.
  4. WMO Secretariat, Recommendations for Wave Observations, Joint WMO/IOC Commission for Oceanography & Marine Meteorolgy(JCOMM), 2007.
  5. T. H. Choo, K. S. Kwak, S. H. Ahn, D. U. Yang, J. K. Son, Development for the function of Wind wave Damage Estimation at the Western Coastal Zone based on Disaster Statistics, Journal of Korea academiaindustrial technology, vol. 18, no. 2, pp. 14-22, 2017a. DOI: http://dx.doi.org/10.5762/KAIS.2017.18.2.14
  6. T. H. Kim, K. H. Kim, J. H. Shim, W. J. Choi, Development of Web-GIS based Real-Time Natural Disaster Damage Information Management System Korea Spatial Information Society, vol. 10, no. 4, pp. 103-107, 2008.
  7. T. H. Choo, G. S. Yun, Y. B. Kwon, S. J. Park, S. R. Kim, Proposal for Wind Wave Damage Cost Estimation at the Southern Coastal Zone based on Disaster Statistics, International JOURNAL OF CONTENTS, vol. 17, no. 4, pp. 267-274, 2017b. DOI: http://dx.doi.org/10.5392/JKCA.2017.17.04.267
  8. H. W. Lee, J. H. Lee, Heavy Seas Forecast using Convolutional Neural Network, Proceedings of KIIS Fall Conference, vol. 24, no. 2, pp. 77-78, 2014.
  9. S. T. Oh, J. D. Lee, J. H. Lee, Heavy Seas Forecast in Korea using Support Vector Machine, Proceedings of KIIS Fall Conference, vol. 23, no. 2, pp. 69-70, 2013.
  10. Korea Institute of Science and Technology, 2001 Report of the Study Construction of Ieodo Ocean Research Station, Ministry of Coeans and Fisheries, 2002.
  11. J. J. Park, Vulnerability and Adaptation to Sea Level Rise and Storm Surge, The Geographical Journal of Korea, vol. 43, no. 3, pp. 435-454, 2009.
  12. D. Y. Um, A Study of Damage District Forecast by Imaginary Tsunami Scenario, Journal of the Korean Association of Geographic Information Studies, vol. 11, no. 1, pp. 105-115, 2008.
  13. S. S. Lee, C. H. Won, Y. M. Kim, Numerical Prediction of Typhoon Storm Surge using Ocean-Meteorology Coupled Model, Journal of the Wind Engineering Institute of Korea, vol. 18, no. 4, pp. 207-214, 2014.
  14. S. H. Ahn, Development for Wind Wave Damage Cost Estimation Considering Coastal Characteristics : West Coast Center, Master Thesis, Pusan National University Graduate School, 2016.
  15. T. H. Choo, J. W. Kwon, G. S. Yun, D. U. Yang, K. S. Kwak, Development of Predicting Function for Wind Wave Damage based on Disaster Statistics: Focused on East Sea and Jeju Island, J. of Korean Society of Environmental Technology, vol. 18, no. 2, pp. 165-172, 2017c.
  16. C. Y. Song, B. S. Yang, Gale Disaster Damage Investigation Process Provement Plan according to Correlation Analysis between Wind Speed and Damage Cost -Centering on Disaster Year Book-, The Korean Society of Safety, vol. 31, no. 2, pp. 119-126, 2016. DOI: https://doi.org/10.14346/JKOSOS.2016.31.2.119
  17. Ministry of Public Safety and Security, Disaster Year Book, 2014-2016
  18. National Emergency Management Agency, Disaster Year Book, 1991-2013
  19. Korea Hydrographic and Oceanographic Agency, The construction results report of Coastal Disaster Assessment System(CDAS), 2015.
  20. National Disaster management potal, Disaster Year Book(1991-2015), http://www.safekorea.go.kr
  21. Korea Meteorological Administration, Marine Weather Buoy, Marine Light House Buoy, Marine Wave Height Buoy(-2015), http://www.kma.go.kr
  22. Korea Hydrographic and Oceanographic Administration, KHOA Smart Tide Forecast, http://www.khoa.go.kr