한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제37권6호
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  • Pages.449-460
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  • 2004
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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기상예보정보를 활용한 월 댐유입량 예측

Monthly Dam Inflow Forecasts by Using Weather Forecasting Information

  • 정대명 (세종대학교 수운연구소) ;
  • 배덕효 (세종대학교 수운연구소·토목환경공학과)
  • Jeong, Dae-Myoung (Waterway Research Institute, Sejong Univ.) ;
  • Bae, Deg-Hyo (Associcate porfessor, Dept. of Sivil & Envionmental Engr. Waterway Rearch Institute, Sejon Univ.)
  • 발행 : 2004.06.01
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 월 댐유입량을 예측하는데 있어서 기상예보정보를 활용한 뉴로-퍼지 시스템의 적용성을 검토하였다. 뉴로-퍼지 알고리즘으로 퍼지이론과 신경망이론의 결합형태인 ANFIS(Adaptive Neuro-Fuzzy Inference System)을 이용하여 모형을 구성하였다. ANFIS의 공간분할에 의한 제어규칙의 선정에 있어 퍼지변수가 증가함에 따라 제어규칙이 기하급수적으로 증가하는 단점을 해결하기 위해 퍼지 클러스터링(Fuzzy Clustering)방법 중 하나인 차감 클러스터링(Subtractive Clustering)을 사용하였다. 또한 본 연구에서는 정성적인 기상예보정보를 정량화 시키는 방법을 제안하였다. AMFIS를 이용하여 월 댐유입량 예측 시, 관측자료만으로 구성된 모형에 의한 예측결과와 관측자료에 기상예보정보를 더하여 구성된 모형에 의한 예측결과를 비교하였다. 그 결과 ANFIS는 기상예보정보를 활용하여 댐유입량을 예측했을 때가 관측자료만으로 예측했을 때보다 예측능력이 더욱 정확함을 보였다.

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.

키워드

참고문헌

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피인용 문헌

  1. Assessment of Typhoon Trajectories and Synoptic Pattern Based on Probabilistic Cluster Analysis for the Typhoons Affecting the Korean Peninsula vol.47, pp.4, 2014, https://doi.org/10.3741/JKWRA.2014.47.4.385