Nonlinear Hydrologic Modeling Using the Stochastic and Neural Networks Approach

The goal of this research is to develop and apply the integrational operation method (IOM) for modeling the relationship of the pan evaporation (PE) and the alfalfa reference evapotranspiration (ETr). Since the observed data of the alfalfa ETr using lysimeter have not been measured for a long time, the Penman-Monteith (PM) method is used to estimate the observed alfalfa ETr. The IOM consists of the combination/application of the stochastic and neural networks models respectively. The stochastic model of Periodic Auto Regressive Moving Average (PARMA) is applied to generate the training dataset for the monthly PE and the alfalfa ETr 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 training pattern is used which can evaluate the suggested climatic variables very well and construct the reliable data for the monthly PE and the alfalfa ETr. Uncertainty analysis is also used to eliminate the climatic variables of input nodes from the 1,000/PARMA(1,1)/GRNNM-GA training pattern. The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes.