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Neural Networks-Genetic Algorithm Model for Modeling of Nonlinear Evaporation and Evapotranspiration Time Series 1. Theory and Application of the Model

비선형 증발량 및 증발산량 시계열의 모형화를 위한 신경망-유전자 알고리즘 모형 1. 모형의 이론과 적용

  • Kim, Sung-Won (Dept. of Rail. and Civil Engr., Dongyang University) ;
  • Kim, Hung-Soo (School of Civil and Environ. Engr. Inha University)
  • 김성원 (동양대학교 철도토목학과) ;
  • 김형수 (인하대학교 환경토목공학부)
  • Published : 2007.01.31

Abstract

The goal of this research is to develop and apply the generalized regression neural networks model(GRNNM) embedding genetic algorithm(GA) for the estimation and calculation of the pan evaporation(PE), which is missed or ungaged and of the alfalfa reference evapotranspiration ($ET_r$), which is not measured in South Korea. Since the observed data of the alfalfa 37. using Iysimeter have not been measured for a long time in South Korea, the Penman-Monteith(PM) method is used to estimate the observed alfalfa $ET_r$. In this research, we develop the COMBINE-GRNNM-GA(Type-1) model for the calculation of the optimal PE and the alfalfa $ET_r$. The suggested COMBINE-GRNNM-GA(Type-1) model is evaluated through training, testing, and reproduction processes. The COMBINE-GRNNM-GA(Type-1) model can evaluate the suggested climatic variables and also construct the reliable data for the PE and the alfalfa $ET_r$. We think that the constructive data could be used as the reference data for irrigation and drainage networks system in South Korea.

본 연구의 목적은 결측 혹은 미계측 증발접시 증발량과 우리나라에서 계측되고 있지 않은 알팔파 기준증발산량의 산정을 위하여 유전자 알고리즘이 내재된 일반화된 회귀신경망모형을 개발하고 적용하는데 있다. 우리나라에서는 장기간동안 증발산계를 이용하여 알팔파 기준증발산량의 관측이 시행되지 않고 있으므로, 본 연구에서는 Penman-Monteith(PM) 공식을 이용하여 산정된 값을 계측된 알팔파 기준증발산량으로 가정하였다. 본 연구를 통하여 최적 증발접시 증발량과 알팔파 기준증발산량의 산정을 위한 COMBINE-GRNNM-GA(Type-1) 모형을 개발하였으며, 훈련, 테스트 및 재현과정을 통하여 COMBINE-GRNNM-GA(Type-1) 모형을 평가하였다. COMBINE-GRNNM-GA (Type-1) 모형은 제시된 기상인자를 평가할 수 있으며, 증발접시 증발량과 알팔파 기준증발산량에 대한 신뢰성 있는 자료를 구축할 수 있다. 더 나아가서 우리나라에서 관개배수 시스템 구축을 위한 참고자료를 제공할 수 있을 것으로 판단된다.

Keywords

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