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The overall average absolute percent error is 2.4%.
An average absolute percent deviation from experiment of 1.0% was obtained over the entire composition range for the eighteen systems.
Statistical and graphical error analyses demonstrated that the developed model can satisfactorily predict the compressibility factor of natural gases with an average absolute percent relative error of 2.3%.
The average absolute percent relative error for all studied systems is 1.03%, which is a satisfactory degree of accuracy for the proposed model.
The average absolute percent relative error (AAPE) for train and test datasets in this study is 2.9e-13 2.9e-13.7% for the RBFN model.
Successful internal and external validations of the model were demonstrated by individual and average absolute percent prediction errors of ≤9% for both Cmax and AUC∞.
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Table 2 shows the average and mean absolute percent error for each correlation through the entire database.
To combine across seasons, the absolute values of the percent errors were averaged providing the mean absolute percent error for the model.
The experimental results indicate that the average values of the mean absolute percent errors of the developed model utilizing 10-min, 30-min and 60-min interval data are 2.8220%, 5.0216% and 7.7205%, respectively, which are much lower than those of the comparison models.
Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient (R), mean absolute percent error (MAPE) and model efficiency (E).
Mean absolute percent errors (MAPE) were computed as the average of the absolute value of the residuals divided by the actual DLW value, multiplied by 100.
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