Degrees of fitness (in terms of R² and RMSE) of modeling calculated using the parameters obtained from the regression equations in Table 6 are presented in Table 7. R²s and RMSEs for the nth-order and limited nth-order models tend to decrease and increase, respectively, as the order n of the models is increased. Both R² and RMSE for the five generic models (from the nth-order model to the combined ‘n + n’ model) are generally superior to the nine existing models. These findings indicate that the introduction of additional calibration parameters and relaxation limitations to chlorine decay order in model structure improve the predictability of chlorine decay behavior substantially even in modeling considering turbulence intensity for parameter evaluation. Chlorine decay models with low order (n ≤ 1) showed good performance in terms of predictability and parsimony of model parameter than those for high order models.