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2021 Vol.17, Issue 2 Preview Page

Original Article

Analysis of Drought Vulnerable Areas using Neural-Network Algorithm

인공신경망 알고리즘을 활용한 가뭄 취약지역 분석

신정훈1ㆍ김준경2*ㆍ염민교3ㆍ김진평4

Jeong Hoon Shin1, Jun Kyeong Kim2*, Min Kyo Yeom3, Jin Pyeong Kim4

1Researcher, Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology, Suwon, Republic of Korea
2Director, Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology, Suwon, Republic of Korea
3Senior Researcher, Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology, Suwon, Republic of Korea
4Director, Computer Vision & Artificial Intelligence Laboratory, Advanced Institute of Convergence Technology, Suwon, Republic of Korea
30 June 2021. pp. 329-340
PDF Citation
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.
Keywords
Artificial Neural Network
Deep-Learning
Drought
Standardized Precipitation Index(SPI)
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 연구결과, 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.
키워드
인공신경망
딥러닝
가뭄
표준강수지수(SPI)
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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 : 17
  • No :2
  • Pages :329-340
  • DOI :https://doi.org/10.15683/kosdi.2021.6.30.329
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