Staff View: Application of Soft Computing in Predicting Groundwater Quality Parameters

Application of Soft Computing in Predicting Groundwater Quality Parameters

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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Hanoon M.S., Ammar A.M., Ahmed A.N., Razzaq A., Birima A.H., Kumar P., Sherif M., Sefelnasr A., El-Shafie A.
Other Authors:	57266877500
Format:	Article
Published:	Frontiers Media S.A. 2023
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.uniten.dspace-26964
record_format	dspace
spelling	my.uniten.dspace-269642023-05-29T17:38:11Z Application of Soft Computing in Predicting Groundwater Quality Parameters Hanoon M.S. Ammar A.M. Ahmed A.N. Razzaq A. Birima A.H. Kumar P. Sherif M. Sefelnasr A. El-Shafie A. 57266877500 57538330200 57214837520 57219410567 23466519000 57206939156 7005414714 6505592467 16068189400 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 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. Final 2023-05-29T09:38:11Z 2023-05-29T09:38:11Z 2022 Article 10.3389/fenvs.2022.828251 2-s2.0-85126715814 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126715814&doi=10.3389%2ffenvs.2022.828251&partnerID=40&md5=b86ca13d822754812aced76c5a94e9df https://irepository.uniten.edu.my/handle/123456789/26964 10 828251 All Open Access, Gold Frontiers Media S.A. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	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 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.
author2	57266877500
author_facet	57266877500 Hanoon M.S. Ammar A.M. Ahmed A.N. Razzaq A. Birima A.H. Kumar P. Sherif M. Sefelnasr A. El-Shafie A.
format	Article
author	Hanoon M.S. Ammar A.M. Ahmed A.N. Razzaq A. Birima A.H. Kumar P. Sherif M. Sefelnasr A. El-Shafie A.
spellingShingle	Hanoon M.S. Ammar A.M. Ahmed A.N. Razzaq A. Birima A.H. Kumar P. Sherif M. Sefelnasr A. El-Shafie A. Application of Soft Computing in Predicting Groundwater Quality Parameters
author_sort	Hanoon M.S.
title	Application of Soft Computing in Predicting Groundwater Quality Parameters
title_short	Application of Soft Computing in Predicting Groundwater Quality Parameters
title_full	Application of Soft Computing in Predicting Groundwater Quality Parameters
title_fullStr	Application of Soft Computing in Predicting Groundwater Quality Parameters
title_full_unstemmed	Application of Soft Computing in Predicting Groundwater Quality Parameters
title_sort	application of soft computing in predicting groundwater quality parameters
publisher	Frontiers Media S.A.
publishDate	2023
_version_	1806428302139719680
score	13.222552

Application of Soft Computing in Predicting Groundwater Quality Parameters

Similar Items