Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River
In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the wate...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2023-07-01
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| Series: | Journal of Engineering |
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| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/2566 |
| _version_ | 1827903831629889536 |
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| author | Rafa Hashim Al Suhili Zainab Jaber Mohammed |
| author_facet | Rafa Hashim Al Suhili Zainab Jaber Mohammed |
| author_sort | Rafa Hashim Al Suhili |
| collection | DOAJ |
| description |
In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential.
The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters. the following (Biological Oxygen Demand( ), Phosphate,( ) Sulfate(), Nitrate( ), Calcium(Ca), Magnesium(Mg), Total Hardness(TH), Potassium(K), Sodium (Na), Chloride (CL), Total Dissolved Solids (TDS), Electric conductivity (EC), Alkalinity(ALK)). The ANN models tried herein were the Multisite- Multivariate ANN models (5-sites, 14 variables), five models were built, one for each of the five stations as the missing data station. The linear
ANN (traditional) models fail to make the prediction of all variables with high correlation coefficient simultaneously. Hence a non- linear input ANN model was developed herein and believed to be a new modification in ANN modeling. It was found that the ANNs have the ability to predict water level and water quality parameters at all the sites with a good degree of accuracy, the range of correlation coefficients obtained are (12.9%-97.2%) for linear models, while for this model with Non-linear terms, The range of correlation coefficients obtained is (71.8%-99.6%).
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| first_indexed | 2024-03-13T00:19:10Z |
| format | Article |
| id | doaj.art-3332bb59473a44438c5d0ae6c89c3f23 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-13T00:19:10Z |
| publishDate | 2023-07-01 |
| publisher | University of Baghdad |
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| series | Journal of Engineering |
| spelling | doaj.art-3332bb59473a44438c5d0ae6c89c3f232023-07-11T18:34:03ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392023-07-01201010.31026/j.eng.2014.10.01Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River Rafa Hashim Al SuhiliZainab Jaber Mohammed In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential. The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters. the following (Biological Oxygen Demand( ), Phosphate,( ) Sulfate(), Nitrate( ), Calcium(Ca), Magnesium(Mg), Total Hardness(TH), Potassium(K), Sodium (Na), Chloride (CL), Total Dissolved Solids (TDS), Electric conductivity (EC), Alkalinity(ALK)). The ANN models tried herein were the Multisite- Multivariate ANN models (5-sites, 14 variables), five models were built, one for each of the five stations as the missing data station. The linear ANN (traditional) models fail to make the prediction of all variables with high correlation coefficient simultaneously. Hence a non- linear input ANN model was developed herein and believed to be a new modification in ANN modeling. It was found that the ANNs have the ability to predict water level and water quality parameters at all the sites with a good degree of accuracy, the range of correlation coefficients obtained are (12.9%-97.2%) for linear models, while for this model with Non-linear terms, The range of correlation coefficients obtained is (71.8%-99.6%). https://joe.uobaghdad.edu.iq/index.php/main/article/view/2566Artificial Neural Network, Water level, The water quality parameters, Tigris River, Non- Linear Model. |
| spellingShingle | Rafa Hashim Al Suhili Zainab Jaber Mohammed Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River Journal of Engineering Artificial Neural Network, Water level, The water quality parameters, Tigris River, Non- Linear Model. |
| title | Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River |
| title_full | Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River |
| title_fullStr | Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River |
| title_full_unstemmed | Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River |
| title_short | Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at Tigris River |
| title_sort | comparison between linear and non linear ann models for predicting water quality parameters at tigris river |
| topic | Artificial Neural Network, Water level, The water quality parameters, Tigris River, Non- Linear Model. |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/2566 |
| work_keys_str_mv | AT rafahashimalsuhili comparisonbetweenlinearandnonlinearannmodelsforpredictingwaterqualityparametersattigrisriver AT zainabjabermohammed comparisonbetweenlinearandnonlinearannmodelsforpredictingwaterqualityparametersattigrisriver |