The Gamma Test has attracted the attention of many researchers in the nonlinear modeling field, especially with Artificial Neural Networks. In theory, the test should provide a modeler with valuable information to find the best input variables without extensive model development for each potential input combination. However, it has been found that the Gamma Test does not always point to the best input combination as validated by the cross validation method. This paper presents a study of using the generalized regression neural network (GRNN) to estimate evaporation. Both the Gamma Test and cross validation are used to find the best model input combination. It has been found that the Gamma Test is not able to identify the best input variables, but the best result is included in the top Gamma value group. The standard error has very valuable information for choosing the group members. This demonstrates that the Gamma Test is still a valuable tool in significantly reducing the modeling workload. The reason for this phenomenon is discussed under the relationship between the Gamma estimate and its stand error. Further research is still needed to explore this relationship in more efficient model input selections.
['Dawei Han', 'Weizhong Yan', 'Alireza Moghaddam Nia']
Uncertainty with the Gamma Test for model input data selection