Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development
A backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance li...
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Taylor & Francis Group
2024-12-01
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| Series: | Water Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23570008.2023.2294535 |
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| author | Zohra Kraiem Kamel Zouari Najiba Chkir |
| author_facet | Zohra Kraiem Kamel Zouari Najiba Chkir |
| author_sort | Zohra Kraiem |
| collection | DOAJ |
| description | A backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers. |
| format | Article |
| id | doaj-art-415728af58ee4f55a8bed74f0536c011 |
| institution | OA Journals |
| issn | 2357-0008 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Water Science |
| spelling | doaj-art-415728af58ee4f55a8bed74f0536c0112025-08-20T01:58:51ZengTaylor & Francis GroupWater Science2357-00082024-12-01381334710.1080/23570008.2023.2294535Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model developmentZohra Kraiem0Kamel Zouari1Najiba Chkir2National Engineering School, Laboratory of Radio-Analyses and Environment, University of Sfax, Sfax, TunisiaNational Engineering School, Laboratory of Radio-Analyses and Environment, University of Sfax, Sfax, TunisiaDepartment of Geography, Faculty of Letters and Humanities, Laboratory of Radio-Analyses and Environment, University of Sfax, Sfax, TunisiaA backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers.https://www.tandfonline.com/doi/10.1080/23570008.2023.2294535Artificial neural networksalinity predictiongroundwater managementChott Djerid shallow aquifersgeochemical assessment |
| spellingShingle | Zohra Kraiem Kamel Zouari Najiba Chkir Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development Water Science Artificial neural network salinity prediction groundwater management Chott Djerid shallow aquifers geochemical assessment |
| title | Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development |
| title_full | Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development |
| title_fullStr | Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development |
| title_full_unstemmed | Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development |
| title_short | Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development |
| title_sort | accurate prediction of salinity in chott djerid shallow aquifers southern tunisia machine learning model development |
| topic | Artificial neural network salinity prediction groundwater management Chott Djerid shallow aquifers geochemical assessment |
| url | https://www.tandfonline.com/doi/10.1080/23570008.2023.2294535 |
| work_keys_str_mv | AT zohrakraiem accuratepredictionofsalinityinchottdjeridshallowaquiferssoutherntunisiamachinelearningmodeldevelopment AT kamelzouari accuratepredictionofsalinityinchottdjeridshallowaquiferssoutherntunisiamachinelearningmodeldevelopment AT najibachkir accuratepredictionofsalinityinchottdjeridshallowaquiferssoutherntunisiamachinelearningmodeldevelopment |