Publication:
Application of Soft Computing in Predicting Groundwater Quality Parameters

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dc.citedby1
dc.contributor.authorHanoon M.S.en_US
dc.contributor.authorAmmar A.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorRazzaq A.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57266877500en_US
dc.contributor.authorid57538330200en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57219410567en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:38:11Z
dc.date.available2023-05-29T09:38:11Z
dc.date.issued2022
dc.description.abstractEvaluating 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 R2 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. Copyright � 2022 Hanoon, Ammar, Ahmed, Razzaq, Birima, Kumar, Sherif, Sefelnasr and El-Shafie.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo828251
dc.identifier.doi10.3389/fenvs.2022.828251
dc.identifier.scopus2-s2.0-85126715814
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85126715814&doi=10.3389%2ffenvs.2022.828251&partnerID=40&md5=b86ca13d822754812aced76c5a94e9df
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26964
dc.identifier.volume10
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleFrontiers in Environmental Science
dc.titleApplication of Soft Computing in Predicting Groundwater Quality Parametersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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