Journal of the Society of Disaster Information (한국재난정보학회 논문집)

The Korean Society of Disaster Information (한국재난정보학회)
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Analysis of Drought Vulnerable Areas using Neural-Network Algorithm

인공신경망 알고리즘을 활용한 가뭄 취약지역 분석

  • Shin, Jeong Hoon (Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology) ;
  • Kim, Jun Kyeong (Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology) ;
  • Yeom, Min Kyo (Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology) ;
  • Kim, Jin Pyeong (Computer Vision & Artificial Intelligence Laboratory, Advanced Institute of Convergence Technology)
  • Received : 2021.04.22
  • Accepted : 2021.05.27
  • Published : 2021.06.30
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Abstract

Purpose: In this paper, using artificial neural network algorithm, the Korean Peninsula was analyzed for drought vulnerable areas by predicting weather data changes. Method: Monthly cumulative precipitation data were utilized for research areas considering the specific nature areas, and weather data prediction through artificial neural network algorithm was carried out using statistical program R. The predicted data were applied to the Standardized Precipitation Index (SPI) to analyze drought vulnerable areas in the Korean Peninsula. Result: In this paper, the correlation coefficient values between real and predicted data are found to be 0.043879 higher on average than the regression results, using artificial neural network algorithms. Conclusion: The results of the research are expected to be used as basic research materials for responding to drought.

연구목적: 본 연구는 인공신경망 라이브러리 기술을 이용하여, 기상 데이터 변화 예측을 통한 한반도 가뭄 취약지역 분석을 목적으로 하였다. 연구방법: 연구지역 중 북한 지역의 다양한 기상데이터의 확보가 힘든 특수성을 고려하여 연구지역의 월별 누적강수량 데이터를 활용하였으며, 통계프로그램 R을 이용하여 인공신경망 알고리즘을 통한 기상데이터 추정을 수행하였다. 연구결과: 본 논문에서 진행한 연구 결과, 실제 데이터와 예측 데이터 간의 상관계수 값은 인공신경망 알고리즘을 활용한 결과가 회귀분석 결과보다 평균 0.043879 더 높은 것으로 확인되었다. 결론: 연구의 결과는 가뭄 대응을 위한 재난대응 기초 연구 자료로 활용 가능할 것으로 기대한다.

Keywords

Acknowledgement

이 논문은 행정안전부 극한재난대응기반기술개발사업의 지원을 받아 수행된 연구임(2020-MOIS31-014).

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