Author(s): Sungwon Kim; Jung-Hun Kim; Ki-Bum Park

Linked Author(s): Sungwon Kim

Keywords: Pan Evaporation; Disaggregation; SVM-NNM; MLP-NNM

Abstract: The purpose of this study 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 support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, the disaggregation means that the yearly PE data should divide into the monthly PE data. And, for the performances of the neural networks models, they are composed of the training, the cross-validation, and the testing data, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

DOI:

Year: 2009