한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제50권10호
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  • Pages.681-690
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  • 2017
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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우리나라에서 일최심신적설의 추정을 위한 인공신경망모형의 활용

Application of Artificial Neural Network for estimation of daily maximum snow depth in Korea

  • 이건 (홍익대학교 토목공학과) ;
  • 이동률 (한국건설기술연구원 수문레이더 재해연구.데이터센터) ;
  • 김동균 (홍익대학교 토목공학과)
  • Lee, Geon (Department of Civil Engineering, Hongik University) ;
  • Lee, Dongryul (Center of Disaster Research and Hydrologic Data Center Using Radar Measurement, Korea Institute of Construction Technology) ;
  • Kim, Dongkyun (Department of Civil Engineering, Hongik University)
  • 투고 : 2017.07.25
  • 심사 : 2017.08.25
  • 발행 : 2017.10.31
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초록

본 연구에서는 우리나라 전역에 대하여 인공신경망 기법을 사용하여 일최심신적설을 추정하였다. 인공신경망 모형 구조를 시행 착오법을 이용하여 설계한 결과, 입력자료는 일 최저 기온, 일 평균 기온, 강수량으로 정하였고, 은닉층과 노드의 수는 각각 1층, 10개로 정하였다. 관측값을 인공신경망의 입력자료로 활용하는 경우, 교차검증 상관계수는 0.87로 Ordinary Kriging기법을 활용하여 일최신심적설을 공간보간한 경우의 교차검증상관계수인 0.40보다 크게 높았다. 미계측 지역의 일최심신적설을 추정하는 경우의 인공신경망 모형의 성능을 알아보기 위하여 인공신경망 모형의 입력자료들을 Ordinary Kriging으로 공간보간하여 일최심신적설을 추정하였다. 이 경우 교차검증 상관계수는 0.49였다. 또한 해발 고도 200 m 이상의 산지에서의 인공신경망의 성능은 나머지 지역인 평지에서의 성능보다 다소 떨어짐을 확인하였다. 본 연구의 이러한 결과는 우리나라 전역에 걸친 정확한 적설량의 즉각적인 산정에 인공신경망 모형이 효과적으로 활용될 수 있음을 의미한다.

This study estimated the daily maximum snow depth using the Artificial Neural Network (ANN) model in Korean Peninsula. First, the optimal ANN model structure was determined through the trial-and-error approach. As a result, daily precipitation, daily mean temperature, and daily minimum temperature were chosen as the input data of the ANN. The number of hidden layer was set to 1 and the number of nodes in the hidden layer was set to 10. In case of using the observed value as the input data of the ANN model, the cross validation correlation coefficient was 0.87, which is higher than that of the case in which the daily maximum snow depth was spatially interpolated using the Ordinary Kriging method (0.40). In order to investigate the performance of the ANN model for estimating the daily maximum snow depth of the ungauged area, the input data of the ANN model was spatially interpolated using Ordinary Kriging. In this case, the correlation coefficient of 0.49 was obtained. The performance of the ANN model in mountainous areas above 200m above sea level was found to be somewhat lower than that in the rest of the study area. This result of this study implies that the ANN model can be used effectively for the accurate and immediate estimation of the maximum snow depth over the whole country.

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