Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq
The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stret...
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| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad
2015-06-01
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| Series: | Journal of Engineering |
| Online Access: | https://www.jcoeng.edu.iq/index.php/main/article/view/420/363 |
| _version_ | 1797708995756032000 |
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| author | Ayad Sleibi Mustafa |
| author_facet | Ayad Sleibi Mustafa |
| author_sort | Ayad Sleibi Mustafa |
| collection | DOAJ |
| description | The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge have the most significant affect on the predicted TDS concentrations. The results showed that a network with (8) hidden neurons was highly accurate in predicting TDS concentration. The correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE) between measured data and model outputs were calculated as 0.975, 113.9 and 11.51%, respectively for testing data sets. Comparisons between final results of ANNs and multiple linear regressions (MLR) showed that the ANNs model could be successfully applied and provides high accuracy to predict TDS concentrations as a water quality parameter. |
| first_indexed | 2024-03-12T06:30:53Z |
| format | Article |
| id | doaj.art-3ff60365a15d494caf19aa28a6285eb3 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-12T06:30:53Z |
| publishDate | 2015-06-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-3ff60365a15d494caf19aa28a6285eb32023-09-03T01:38:58ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392015-06-01216162179Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, IraqAyad Sleibi MustafaThe study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge have the most significant affect on the predicted TDS concentrations. The results showed that a network with (8) hidden neurons was highly accurate in predicting TDS concentration. The correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE) between measured data and model outputs were calculated as 0.975, 113.9 and 11.51%, respectively for testing data sets. Comparisons between final results of ANNs and multiple linear regressions (MLR) showed that the ANNs model could be successfully applied and provides high accuracy to predict TDS concentrations as a water quality parameter.https://www.jcoeng.edu.iq/index.php/main/article/view/420/363 |
| spellingShingle | Ayad Sleibi Mustafa Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq Journal of Engineering |
| title | Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq |
| title_full | Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq |
| title_fullStr | Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq |
| title_full_unstemmed | Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq |
| title_short | Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq |
| title_sort | artificial neural networks modeling of total dissolved solid in the selected locations on tigris river iraq |
| url | https://www.jcoeng.edu.iq/index.php/main/article/view/420/363 |
| work_keys_str_mv | AT ayadsleibimustafa artificialneuralnetworksmodelingoftotaldissolvedsolidintheselectedlocationsontigrisriveriraq |