# A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water
## 4 Conclusions  
In this article, the ANN and SR models were proposed for multivariate calibration and analysis using the experimental data obtained from the OVAT experimentation. It was observed that the ANN was the better model for this application after comparing the performance of the two models. For each dataset, the standard deviations were calculated and applied to the experimental and the predicted values. The experimental and ANN-predicted values were discovered to be very close. A linear curve was produced when the experimental data were plotted against predicted values for the training dataset. The correlation coefficient, R\({}^{2}\), between experimental and ANN predicted values was 96.9%, whereas the correlation coefficient between experimental and SR predicted values was 73.59% for the training dataset. This further supports the ability of our ANN model to predict lindane removal with accuracy. The computed correlation coefficients for the testing dataset--89.9% for ANN and 67.75% for SR--showed a stronger correlation between the actual and ANN predicted values. The RMSE values for the training and testing datasets for the ANN model were 1.482 and 2.402, respectively, which were noticeably lower than the corresponding SR model RMSE values of 4.035 and 3.890. It was also found that the MAPE values for the training and testing datasets for the ANN model, which were 0.018 and 0.031, respectively, were noticeably lower than those for the SR model. The RSR and PBIAS values, which represent the strength of the developed model, are also low for both the training and testing datasets. However, the R\({}^{2}\) and WIA values are higher than 0.90 for both datasets, which further attests to the effectiveness of the developed ANN model. Therefore, it is believed that the overhead expenses related to the water treatment process are expected to decrease due to the application of our developed ANN model. The application will lower the cost of eliminating several inorganic and organic contaminants from the contaminated water, including lindane, and will optimize the use of chemicals.  
Conceptualization, A.A.A., M.S., M.K.N. and S.M.R.; methodology, M.K.N. and M.S.; software, M.S. and A.A.A.; formal analysis, M.S. and S.M.R.; resources, M.K.I. and M.M.I.; data curation, M.S. and M.M.R.; writing--original draft preparation, A.A.A., M.S. and M.M.R., writing--review and editing, M.M.R., A.R., M.K.N., S.M.R. and A.A.A.; supervision, M.K.N. and M.S.; project administration, M.S. and M.M.R.; funding acquisition, M.M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at King Faisal University (KFU), Al-Ahsa 31982, Saudi Arabia, through Project No. GRANT 3415.  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors, M.M.R. ([EMAIL_ADDRESS]) and M.S. ([EMAIL_ADDRESS]), upon reasonable request.  
The authors acknowledge the support received from King Faisal University (KFU) and King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia.  
The authors declare no conflict of interest.