# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 3 Results and Discussion
### Parametric Importance Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Heavy Metal** & **Model** & **Evaluation Metrics** & **Reference** \\ \hline Chromium & ANFIS & \begin{tabular}{c} R\({}^{2}\) = 0.9997 \\ MSE = 1.288 \(\times\) 10\({}^{-06}\) \\ \end{tabular} & [PERSON] et al. (2020)[25] \\ \hline Copper & ANFIS & \begin{tabular}{c} R\({}^{2}\) = 0.99 \\ MSE = 0.707 \\ \end{tabular} & [PERSON] et al. (2018)[26] \\ \hline Copper & ANFIS & \begin{tabular}{c} R\({}^{2}\) = 0.9024 \\ MSE = 3.29 \\ \end{tabular} & [PERSON] et al. (2020)[27] \\ \hline Arsenic & ANN & \begin{tabular}{c} R\({}^{2}\) = 0.9975 \\ Relative \(\%\) error = 0.293 \\ \end{tabular} & [PERSON] et al. (2015)[15] \\ \hline Arsenic & ANN & \begin{tabular}{c} R\({}^{2}\) = 0.9998 \\ \end{tabular} & [PERSON] et al. (2016)[16] \\ \hline Arsenic & ANN & \begin{tabular}{c} R\({}^{2}\) = 0.9984 \\ MSE = 0.036 \\ \(\%\) Error (abs) = 3.16 \\ \end{tabular} & [PERSON] et al. (2017)[18] \\ \hline Arsenic & \begin{tabular}{c} R\({}^{2}\) = 0.9984 \\ MSE = 0.036 \\ \(\%\) Error (abs) = 3.16 \\ \end{tabular} & \begin{tabular}{c} R\({}^{2}\) = 0.954 \\ RMSE = 2.8 \\ \(\%\) Error (abs) = 0.96 \\ \end{tabular} & \begin{tabular}{c} R\({}^{2}\) = 0.958 \\ \(\%\) Error (abs) = 0.93 \\ \end{tabular} \\ \hline Arsenic & RF, and SVR & \begin{tabular}{c} R\({}^{2}\) = 0.933 \\ MSE = 0.137 \\ RMSE = 0.274 \\ \end{tabular} & \
\begin{tabular}{c} [PERSON] et al. (2021)[21] \\ \end{tabular} \\ \hline Arsenic & ANFIS &
\begin{tabular}{c} R\({}^{2}\) = 0.933 \\ MSE = 0.137 \\ RMSE = 0.274 \\ \end{tabular} & Current Study \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative performance of different MLAs in predicting the adsorption of heavy metals. adsorbent dosage ranked fourth in terms of relative importance with a total score of 305. As the adsorbent dosage increases, the mass transfer surface area also increases for the same particle size. It enhances the adsorption efficiency. However, economic adsorbent mass, i.e., minimum mass with the highest adsorbability, should be used to achieve the optimum adsorption. Similarly, when using biosorbents, as the inoculum size increases, the adsorption efficiency increases. Therefore, the inoculum size ranked fifth in terms of relative importance with a total score of 270. Furthermore, the nature and the spontaneity of the adsorption process have a significant effect on the removal efficiency. Temperature plays a key role in the determination of the thermodynamic properties of the adsorption process and thus has a non-negligible impact. Temperature ranked sixth in terms of relative importance with a total score of 237. The rest of the parameters, namely, agitation speed, flow rate, and solution volume showed less significant impacts on the _As_ removal efficiency. Their evaluation score varied between 63 and 76. That is, all the parameters selected for the current study had varying contributions in predicting the output. Figure 5: Importance of the experimental parameters on the adsorption efficiency: (**a**) dataset 1, (**b**) dataset 2, (**c**) dataset 3, (**d**) dataset 4, (**e**) dataset 5, (**f**) dataset 6, and (**g**) dataset 7.