ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region
Estimating groundwater salinity is important for the use of groundwater resources for irrigation purposes and provides a suitable guide for the management of groundwater. In this study, the artificial neural networks (ANNs) were adopted to estimate the salinity of groundwater identified by total dis...
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-03-01
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| Series: | Open Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/eng-2022-0025 |
| _version_ | 1811203708398075904 |
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| author | Al-Waeli Luay Kadhim Sahib Jumana Hadi Abbas Hiba Ali |
| author_facet | Al-Waeli Luay Kadhim Sahib Jumana Hadi Abbas Hiba Ali |
| author_sort | Al-Waeli Luay Kadhim |
| collection | DOAJ |
| description | Estimating groundwater salinity is important for the use of groundwater resources for irrigation purposes and provides a suitable guide for the management of groundwater. In this study, the artificial neural networks (ANNs) were adopted to estimate the salinity of groundwater identified by total dissolved solids (TDS), sodium adsorption ratio (SAR), and sodium (Na+) percent, using electrical conductivity, magnesium (Mg2+), calcium (Ca2+), potassium (K+), and potential of hydrogen (pH) as input elements. Samples of groundwater were brought from 51 wells situated in the plateau of Najaf–Kerbala provinces. The network structure was designed as 6-4-3 and adopted the default scaled conjugate gradient algorithm for training using SPSS V24 software. It was observed that the proposed model with four neurons was exact in estimating the irrigation salinity. It has shown a suitable agreement between experimental and ANN values of irrigation salinity indices for training and testing datasets based on statistical indicators of the relative mean error and determination coefficient R
2 between ANN outputs and experimental data. TDS, SAR, and Na percent predicted output tracked the measured data with an R
2 of 0.96, 0.97, and 0.96 with relative error of 0.038, 0.014, and 0.021, respectively, for testing, and R
2 of 0.95, 0.96, and 0.96 with relative error of 0.053, 0.065, and 0.133, respectively, for training. This is an indication that the designed network was satisfactory. The model could be utilized for new data to predict the groundwater salinity for irrigation purposes at the Najaf–Kerbala plateau in Iraq. |
| first_indexed | 2024-04-12T02:59:23Z |
| format | Article |
| id | doaj.art-400c4a2044204831ba177d75b9273f0b |
| institution | Directory Open Access Journal |
| issn | 2391-5439 |
| language | English |
| last_indexed | 2024-04-12T02:59:23Z |
| publishDate | 2022-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Engineering |
| spelling | doaj.art-400c4a2044204831ba177d75b9273f0b2022-12-22T03:50:42ZengDe GruyterOpen Engineering2391-54392022-03-0112112012810.1515/eng-2022-0025ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala regionAl-Waeli Luay Kadhim0Sahib Jumana Hadi1Abbas Hiba Ali2Civil Engineering Department, University of Kufa, Faculty of Engineering, Najaf Governorate, Kufa, IraqCivil Engineering Department, University of Kufa, Faculty of Engineering, Najaf Governorate, Kufa, IraqCivil Engineering Department, University of Kufa, Faculty of Engineering, Najaf Governorate, Kufa, IraqEstimating groundwater salinity is important for the use of groundwater resources for irrigation purposes and provides a suitable guide for the management of groundwater. In this study, the artificial neural networks (ANNs) were adopted to estimate the salinity of groundwater identified by total dissolved solids (TDS), sodium adsorption ratio (SAR), and sodium (Na+) percent, using electrical conductivity, magnesium (Mg2+), calcium (Ca2+), potassium (K+), and potential of hydrogen (pH) as input elements. Samples of groundwater were brought from 51 wells situated in the plateau of Najaf–Kerbala provinces. The network structure was designed as 6-4-3 and adopted the default scaled conjugate gradient algorithm for training using SPSS V24 software. It was observed that the proposed model with four neurons was exact in estimating the irrigation salinity. It has shown a suitable agreement between experimental and ANN values of irrigation salinity indices for training and testing datasets based on statistical indicators of the relative mean error and determination coefficient R 2 between ANN outputs and experimental data. TDS, SAR, and Na percent predicted output tracked the measured data with an R 2 of 0.96, 0.97, and 0.96 with relative error of 0.038, 0.014, and 0.021, respectively, for testing, and R 2 of 0.95, 0.96, and 0.96 with relative error of 0.053, 0.065, and 0.133, respectively, for training. This is an indication that the designed network was satisfactory. The model could be utilized for new data to predict the groundwater salinity for irrigation purposes at the Najaf–Kerbala plateau in Iraq.https://doi.org/10.1515/eng-2022-0025groundwater qualitynajaf–kerbala plateauartificial neural network |
| spellingShingle | Al-Waeli Luay Kadhim Sahib Jumana Hadi Abbas Hiba Ali ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region Open Engineering groundwater quality najaf–kerbala plateau artificial neural network |
| title | ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region |
| title_full | ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region |
| title_fullStr | ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region |
| title_full_unstemmed | ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region |
| title_short | ANN-based model to predict groundwater salinity: A case study of West Najaf–Kerbala region |
| title_sort | ann based model to predict groundwater salinity a case study of west najaf kerbala region |
| topic | groundwater quality najaf–kerbala plateau artificial neural network |
| url | https://doi.org/10.1515/eng-2022-0025 |
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