# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 3 Results and Discussion
### Parametric Importance Analysis  
The impact of nine parameters on the \(As\) removal percentage was investigated in this study. However, all experimental parameters are not expected to have similar impacts on predicting the output. Therefore, the relative importance of each parameter in each dataset was quantified using the values of R%. The higher the R% value, the higher the importance of the parameter. Among all the investigated parameters, pH, the \(As\) initial concentration, and contact time were found as the most influential parameters with different impacts. While pH scored the highest of 575, the initial concentration and contact time scored 568 and 445, respectively. Figure 5 shows the importance of each input parameter on the efficiency of \(As\) adsorption for all datasets. The current investigation provides a comparative ranking of the most dominating parameters in modeling the arsenic adsorption process: pH > arsenate initial concentration > contact time > adsorbent mass > inoculum size > temperature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **MSE** & **RMSE** & **R (\%)** & **R\({}^{2}\)** \\ \hline
1 & 8.96 \(\times\) 10\({}^{-4}\) & 2.994 \(\times\) 10\({}^{-2}\) & 99.16 & 0.9580 \\
2 & 0.2260 & 0.4754 & 92.53 & 0.9423 \\
3 & 4.4564 \(\times\) 10\({}^{-2}\) & 0.2111 & 95.82 & 0.9054 \\
4 & 2.69 \(\times\) 10\({}^{-3}\) & 5.186 \(\times\) 10\({}^{-2}\) & 99.97 & 0.9226 \\
5 & 0.4770 & 0.6907 & 96.69 & 0.9114 \\
6 & 0.207 & 0.4549 & 99.94 & 0.9490 \\
7 & 5.4221 \(\times\) 10\({}^{-5}\) & 7.36 \(\times\) 10\({}^{-3}\) & 99.94 & 0.9443 \\ \hline
**Average** & **0.137** & **0.274** & **97.72** & **0.9333** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Testing results of the ANFIS model. Figure 4: Performance of the ANFIS model in the testing phase: (**a**) dataset 1, (**b**) dataset 2, (**c**) dataset 3, (**d**) dataset 4, (**e**) dataset 5, (**f**) dataset 6, and (**g**) dataset 7. Generally, as the pH increases, the concentration of H\({}^{+}\) ions decreases. The solution H\({}^{+}\) ions can compete for the available total active sites, thus reducing the active sites available for the \(As\) ions. Therefore, increasing pH can increase the removal percentage of \(As\)[46]. However, there is an optimum value beyond which any further increase in pH will not affect the adsorption efficiency. Therefore, the value of pH must be optimized before conducting the batch adsorption experiments. In addition, the arsenate removal percentage can be calculated using Equation (14). For the same adsorbent dosage (mass), as the adsorbate initial concentration increases, the removal percentage decreases [46]. An adsorbent has a limited number of active adsorption sites at which \(As\) ions are adsorbed. Therefore, insufficient active sites will be the case as the \(As\) concentration increases. Therefore, the initial concentration has a significant impact on the adsorption process. \[Arsenic\ removal\ (\%)=\frac{\left(C_{o}-C_{f}\right)}{C_{o}}\ \times\ 100 \tag{14}\]  
where:  
\(C_{o}\) is the \(As\) initial concentration (mg/L); and  
\(C_{f}\) is the \(As\) concentration at the end of the experiment (mg/L). Moreover, contact time is one of the dominating parameters in the adsorption process. As the contact time increases, the adsorption efficiency increases until reaching equilibrium, at which no further mass transfer will occur. The optimum contact time must be determined before conducting experiments.