한국수자원학회:학술대회논문집 (Proceedings of the Korea Water Resources Association Conference)
- 한국수자원학회 2010년도 학술발표회
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- Pages.1346-1349
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- 2010
추계학적 신경망 접근법을 이용한 수문학적 시계열의 모형화
Modeling of Hydrologic Time Series using Stochastic Neural Networks Approach
- 발행 : 2010.05.10
초록
The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.