Integrational Operation Method Using Stochastic/Neural Networks Models

The goal of this research is to develop and apply the integrational operation method (IOM) for the modeling of the pan evaporation (PE) and the alfalfa reference evapotranspiration (ET_r). Since the observed data of the alfalfa ET_r using lysimeter have not been measured for a long time, South Korea, the Penman-Monteith (PM) method is used to estimate the observed alfalfa ET_r. The IOM consists of the application of the stochastic/neural networks models respectively. The stochastic model is applied to generate the training dataset for the monthly PE and the alfalfa ET_r, and the neural networks models are applied to calculate the observed test dataset reasonably. Among the six training patterns, 1,000/PARMA(1,1)/GRNNM-GA can evaluate the suggested climatic variables very well and also calculate the reliable dataset for the monthly PE and the alfalfa ET_r. Uncertainty analysis is used to eliminate the climatic variables of the input nodes from 1,000/PARMA(1,1)/GRNNM-GA. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. Finally, the IOM is developed to model the monthly PE and the alfalfa ET_r with the least cost and endeavor.