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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...

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Main Authors: Hanoon, Marwah Sattar, Ammar, Amr Moftah, Ahmed, Ali Najah, Razzaq, Arif, Birima, Ahmed H., Kumar, Pavitra, Sherif, Mohsen, Sefelnasr, Ahmed, El-Shafie, Ahmed
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Published: Frontiers Media SA 2022
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GE Environmental Sciences
Online Access:http://eprints.um.edu.my/33338/
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spelling my.um.eprints.333382022-08-08T07:16:02Z http://eprints.um.edu.my/33338/ Application of soft computing in predicting groundwater quality parameters Hanoon, Marwah Sattar Ammar, Amr Moftah Ahmed, Ali Najah Razzaq, Arif Birima, Ahmed H. Kumar, Pavitra Sherif, Mohsen Sefelnasr, Ahmed El-Shafie, Ahmed GE Environmental Sciences 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. Frontiers Media SA 2022-02-28 Article PeerReviewed 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 <https://doi.org/10.3389/fenvs.2022.828251>. 10.3389/fenvs.2022.828251
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic GE Environmental Sciences
spellingShingle GE Environmental Sciences
Hanoon, Marwah Sattar
Ammar, Amr Moftah
Ahmed, Ali Najah
Razzaq, Arif
Birima, Ahmed H.
Kumar, Pavitra
Sherif, Mohsen
Sefelnasr, Ahmed
El-Shafie, Ahmed
Application of soft computing in predicting groundwater quality parameters
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 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.
format Article
author Hanoon, Marwah Sattar
Ammar, Amr Moftah
Ahmed, Ali Najah
Razzaq, Arif
Birima, Ahmed H.
Kumar, Pavitra
Sherif, Mohsen
Sefelnasr, Ahmed
El-Shafie, Ahmed
author_facet Hanoon, Marwah Sattar
Ammar, Amr Moftah
Ahmed, Ali Najah
Razzaq, Arif
Birima, Ahmed H.
Kumar, Pavitra
Sherif, Mohsen
Sefelnasr, Ahmed
El-Shafie, Ahmed
author_sort Hanoon, Marwah Sattar
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 SA
publishDate 2022
url http://eprints.um.edu.my/33338/
_version_ 1740826022787416064
score 13.211869

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