KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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The Temporal Disaggregation Model for Nonlinear Pan Evaporation Estimation

비선형 증발접시 증발량 산정을 위한 시간적 분해모형

  • 김성원 (동양대학교 철도토목학과) ;
  • 김정헌 (동양대학교 대학원 철도토목학과) ;
  • 박기범 (동양대학교 철도토목학과) ;
  • 김형수 (인하대학교 사회기반시스템공학부)
  • Received : 2010.03.15
  • Accepted : 2010.07.06
  • Published : 2010.08.31
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Abstract

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

본 연구의 목적은 연 증발접시 증발량의 시간적인 분해를 위하여 신경망모형을 적용하는데 있다. 신경망모형은 각각 다층 퍼셉트론 신경망모형(MLP-NNM)과 일반화된 회귀신경망모형(GRNNM)으로 구성되어 있다. 그리고 신경망모형의 수행평가를 위하여 훈련 및 테스트과정으로 구성되었다. 신경망모형의 훈련과정을 위하여 실측, 모의 및 혼합자료와 같은 세 가지 형태의 자료가 사용되었으며, 테스트과정을 위해서는 실측자료만 이용되었다. 본 연구를 통하여 비선형 시계열자료의 시간적 분해를 위해서 MLP-NNM과 GRNNM의 적용성을 평가하였다. 게다가 연 증발접시 증발량 자료의 시간적 분해로부터 신뢰성있는 월 증발접시 증발량자료를 구축할 수 있을 것이며, 관개배수 네트워크 시스템의 평가를 위한 이용가능한 자료를 제공할 수 있을 것이다.

Keywords

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