I want to run 5 different models to estimate stream flow. In order to optimize the characteristics of these models I use Taguchi method. So I have to run different models according to the Taguchi orthogonal array. Therefore, I have different models with different inputs and different data lengths. For example the first test is: using rainfall and temperature in ANFIS model with 2 year data length, while the second test is: using rainfall, temperature and discharge for previous day in SVR model with 10 year data length. So, the inputs, Data length and model type is changing in these tests. What is the best performance evaluation criterion for this study? NRMSE can be a good criterion because it normalizes the RMSE and in this way, it removes the effect of data range.
Now, I want to know if there is any better solution for this problem.