Table 2.

Error functions and its equations with references.

Error function	Equation	Reference
Mean-squared error	$MSE=\frac{1}{n}\sum _{i=1}^n{\left(Y_{i,p}-Y_{i,e}\right)}^2\left(6\right)$	(Wu et al., 2021)
Root mean square error	$RMSE=\sqrt{\frac{\sum _{i=1}^n{\left(Y_{i,e}-Y_{i,p}\right)}^2}{n}}\left(7\right)$	(Nasrudin et al., 2020)
Standard error of prediction	$SEP=\frac{RMSE}{Y_e}\times 100\left(8\right)$	(Gendy et al., 2020)
Coefficient of determination	$R^2=1-\frac{\sum _{i=1}^n{\left(Y_{i,p}-Y_{i,e}\right)}^2}{\sum _{i=1}^n{\left(Y_{i,e}-Y_{i,p}\right)}^2}\left(9\right)$	(Shukla et al., 2020)
Mean absolute error	$MAE=\frac{1}{n}\sum _{i=1}^n\left|Y_{i,e}-Y_{i,p}\right|\left(10\right)$	(Awolusi et al., 2019)
Absolute average relative deviation	$AARD=\frac{1}{n}\sum _{i=1}^n\left(\left|\frac{Y_{i,p}-Y_{i,e}}{Y_{i,e}}\right|\right)x100\left(11\right)$	(Sodeifian et al., 2015)

where, Y_i,_e is the experimental data, Y_i,p is the predicted data obtained from either RSM or ANN, Y_e is the mean value of experimental data and n is the number of the experimental data. The final network was selected based on the lowest training error and depending upon the test data.