Nonlinear Analysis using the Artificial Neural Networks Model

The purpose of this study is to apply the genetic algorithm (GA) of the generalized regression neural networks model (GRNNM) in order to estimate and calculate the pan evaporation (PE), which is missing or ungauged, and the alfalfa reference evapotranspiration (ET r ), which is not measured, in South Korea. Since the observed data of the alfalfa ET r using lysimeter 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 study, we develop the COMBINE-GRNNM-GA (Type-1) model for calculating the reasonable PE and the alfalfa ET r . The suggested COMBINE-GRNNM-GA (Type-1) model is evaluated through training, testing, and reproduction performances. The COMBINEGRNNM-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 can be used as the reference data for irrigation and drainage networks system in South Korea.