Hanoon, Marwah Sattar and Ammar, Amr Moftah and Ahmed, Ali Najah and Razzaq, Arif and Birima, Ahmed H. and Kumar, Pavitra and Sherif, Mohsen and Sefelnasr, Ahmed and El-Shafie, Ahmed (2022) Application of soft computing in predicting groundwater quality parameters. Frontiers In Environmental Science, 10. ISSN 2296-665X, DOI https://doi.org/10.3389/fenvs.2022.828251.
Full text not available from this repository.Abstract
Evaluating the quality of groundwater in a specific aquifer could be a costly and time-consuming procedure. An attempt was made in this research to predict various parameters of water quality called Fe, Cl, SO4, pH and total hardness (as CaCO3) by measuring properties of total dissolved solids (TDSs) and electrical conductivity (EC). This was reached by establishing relations between groundwater quality parameters, TDS and EC, using various machine learning (ML) models, such as linear regression (LR), tree regression (TR), Gaussian process regression (GPR), support vector machine (SVM), and ensembles of regression trees (ER). Data for these variables were gathered from five unrelated groundwater quality studies. The findings showed that the TR, GPR, and ER models have satisfactory performance compared to that of LR and SVM with respect to different assessment criteria. The ER model attained higher accuracy in terms of R-2 in TDS 0.92, Fe 0.89, Cl 0.86, CaCO3 0.87, SO4 0.87, and pH 0.86, while the GPR model attained an EC 0.98 compared to all developed models. Moreover, comparisons among the different developed models were performed using accuracy improvement (AI), improvement in RMSE (PRMSE), and improvement in PMAE to determine a higher accuracy model for predicting target properties. Generally, the comparison of several data-driven regression methods indicated that the boosted ensemble of the regression tree model offered better accuracy in predicting water quality parameters. Sensitivity analysis of each parameter illustrates that CaCO3 is most influential in determining TDS and EC. These results could have a significant impact on the future of groundwater quality assessments.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Groundwater quality; Machine learning; Linear regression; Tree regression; Support vector machine |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 08 Aug 2022 07:16 |
| Last Modified: | 08 Aug 2022 07:16 |
| URI: | http://eprints.um.edu.my/id/eprint/33338 |
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