[논문]인공신경망 알고리즘을 활용한 가뭄 취약지역 분석

신정훈; 김준경; 염민교; 김진평

doi:10.15683/kosdi.2021.6.30.329

[국내논문] 인공신경망 알고리즘을 활용한 가뭄 취약지역 분석
Analysis of Drought Vulnerable Areas using Neural-Network Algorithm 원문보기

한국재난정보학회논문집 = Journal of the Society of Disaster Information, v.17 no.2 = no.52, 2021년, pp.329 - 340

신정훈 (Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology) , 김준경 (Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology) , 염민교 (Safety Inspection for Infrastructure Laboratory, Advanced Institute of Convergence Technology) , 김진평 (Computer Vision & Artificial Intelligence Laboratory, Advanced Institute of Convergence Technology)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (11)

그림 Fig. 1. Research flow diagram
그림 Fig. 2. SPI 3 Nugar precipitation time series process
표 Table 1. Four typical drought classifications and drought indexes
그림 Fig. 3. Artificial neural networks, backpropagation
그림 Fig. 4. Cluster analysis results
그림 Fig. 5. Neuralnet model in R, Ulsan & Gwangju & Busan
그림 Fig. 6. Neuralnet model in R, Suwon & Gangneung & Cheonan
그림 Fig. 7. Artificial neural network learning and analysis results, Ulsan & Gwangju
그림 Fig. 8. Artificial Neural Network Learning and Analysis Results, Busan & Suwon
그림 Fig. 9. Artificial Neural Network Learning and Analysis Results, Gangneung & Cheonan
표 Table 2. Comparing analysis results with regression and neuralnet models

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