# A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water
## 3 Results and Discussion  
Table 2 compares the predicted values of lindane removal efficiency under optimal conditions for each developed model with the experimentally measured values of lindane removal efficiency. The training records are between 1 and 38, and the remaining records were used for testing the model. The standard deviations (SD) were computed for each data set and applied to both the experimental and the predicted values. The ANN-predicted values were found to be much closer to the experimental values. There was very little difference between the two of them, which was mirrored by the fact that there was minimal variation in the ANN SD values [37]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Neurons**} & \multicolumn{2}{c}{**MAPE**} \\ \cline{2-3}  & **Training** & **Testing** \\ \hline
1 & 0.052535468 & 0.05111675 \\ \hline
2 & 0.051537307 & 0.050463394 \\ \hline
3 & 0.048195353 & 0.050449361 \\ \hline
4 & 0.021337861 & 0.031621683 \\ \hline
5 & 0.023813819 & 0.048887605 \\ \hline
6 & 0.016572092 & 0.053637026 \\ \hline
7 & 0.016127481 & 0.036106327 \\ \hline
8 & 0.01671685 & 0.046555818 \\ \hline
9 & 0.015428085 & 0.04684183 \\ \hline
10 & 0.010350745 & 0.050320977 \\ \hline
11 & 0.009477217 & 0.032474059 \\ \hline
12 & 0.011147193 & 0.034483183 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Minimization of MAPE. Figure 5: MAPE Values for the Inputs. on zeolite [40]. In the same vein, Figure 6b shows the experimental and predicted values results for the testing data set. The figure shows how well the experimental values and the ANN-predicted values agreed. To determine the degree of association or correlation coefficient between the two sets of values, the ANN and SR predicted values obtained from the developed models for the testing datasets were also plotted against experimental values (Figure 7b). The testing dataset obtained a correlation coefficient of 89.9% for ANN and 67.75% for SR, which showed a stronger association between the actual and ANN-predicted values. Figure 6: Line plots for the training dataset (**a**) and the testing dataset (**b**). Additionally, RMSE, MAPE, and RSR are tools used to evaluate the error between the modeled values and experimental values. Lower RMSE, MAPE, and RSR values indicate a better model [41]. In contrast, the R\({}^{2}\) and WIA values vary from '0' to '1', where the number '1' indicates a perfect match between the actual and the estimated variables, whereas the number' 0' exhibits the output variables cannot be estimated from the given input variables. Besides, the ideal value of PBIAS is 0.0, which is a perfect match between the estimated and the actual variables. However, positive and negative values of PBIAS refer to underestimation and overestimation of the estimated outputs, respectively [42; 43; 44; 45; 46]. Table 3 presents the performance measuring parameters. RMSE, MAPE, and RSR represent a measure of the variances between values predicted by a model. The difference between experimental values and predicted values increases as the values rise. The ANN model's RMSE values for the training and testing datasets were 1.482 and 2.402, respectively, which were significantly lower compared to the SR model's RMSE values of 4.035 and 3.890 for training and testing, respectively. This was the conclusion of [PERSON] et al. Figure 7: Scatter plots for the training dataset (**a**) and the testing dataset (**b**). when they used ANN to study the adsorptive removal of arsenic [47]. Additionally, it was noted that the ANN model's MAPE values for the training and testing datasets, which were 0.018 and 0.031, respectively, were significantly lower than the SR model's MAPE values, as shown in Table 3 and presented in Figure 8. Likewise, the RSR and PBIAS values are low for both training and testing datasets, indicating the developed model's strength. On the other hand, the R\({}^{2}\) and WIA values are more than 0.90 for both datasets, which also testify to the efficacy of the developed ANN model.