USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA)
Lake Fetzara is one of the important lakes in the northeast of Algeria; the water supplying this lake comes from different precipitation and wadis. Moreover, Meboudja wadi constitutes the drainage channel. The water of the lake and the underlying groundwater is exposed to excessive overuse; which se...
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| Format: | Article |
| Language: | English |
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Stefan cel Mare University of Suceava
2022-06-01
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| Series: | Food and Environment Safety |
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| Online Access: | http://fens.usv.ro/index.php/FENS/article/view/887/792 |
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| author | Zahra BOUHALI Larbi DJABRI Hamza BOUGUERRA Fatma TRABELSI Azzedine HANI Hicham CHAFFAI |
| author_facet | Zahra BOUHALI Larbi DJABRI Hamza BOUGUERRA Fatma TRABELSI Azzedine HANI Hicham CHAFFAI |
| author_sort | Zahra BOUHALI |
| collection | DOAJ |
| description | Lake Fetzara is one of the important lakes in the northeast of Algeria; the water supplying this lake comes from different precipitation and wadis. Moreover, Meboudja wadi constitutes the drainage channel. The water of the lake and the underlying groundwater is exposed to excessive overuse; which seriously threatens the hydrological and ecological balance. The overexploitation is explained by the increase in water mineralization, which poses a risk of soil salinization. To this end, this article deals with the subject of current salinity and predict its evolution over time by means of the modeling of the artificial neural network (ANN), according to the period of low water and the period of high water. The ANN were trained using three different algorithms: the Scaled Conjugate Gradient back propagation (SCG) algorithm and One Step Secant back propagation (OSS) algorithm and Quasi-Newton algorithm (BFGS). The performance results indicate that the three algorithms provided satisfactory simulations according to the determination coefficient (R2) and the performance criteria of the mean square error (RMSE), with priority to the BFGS algorithm; where the coefficient of determination using the BFGS algorithm varies between 69.5% and 95.3%. The BFGS method presents better results in order to design appropriate institutional mechanisms, capable of leading to the protection of the quality of these resources essential to the promotion of sustainable development. |
| first_indexed | 2024-04-12T22:59:37Z |
| format | Article |
| id | doaj.art-e72823c4647847a586851281e8f13e40 |
| institution | Directory Open Access Journal |
| issn | 2068-6609 2559-6381 |
| language | English |
| last_indexed | 2024-04-12T22:59:37Z |
| publishDate | 2022-06-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Food and Environment Safety |
| spelling | doaj.art-e72823c4647847a586851281e8f13e402022-12-22T03:13:05ZengStefan cel Mare University of SuceavaFood and Environment Safety2068-66092559-63812022-06-01212107115USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA)Zahra BOUHALI0Larbi DJABRI1Hamza BOUGUERRA2Fatma TRABELSI3Azzedine HANI4Hicham CHAFFAI5Faculty of earth sciences, Badji Mokhtar University,Annaba,AlgeriaFaculty of earth sciences, Badji Mokhtar University,Annaba,AlgeriaFaculty of earth sciences, Badji Mokhtar University,Annaba,AlgeriaMedjez Elbab Engineering School,Djendouba University. TunisiaFaculty of earth sciences, Badji Mokhtar University,Annaba,AlgeriaFaculty of earth sciences, Badji Mokhtar University,Annaba,AlgeriaLake Fetzara is one of the important lakes in the northeast of Algeria; the water supplying this lake comes from different precipitation and wadis. Moreover, Meboudja wadi constitutes the drainage channel. The water of the lake and the underlying groundwater is exposed to excessive overuse; which seriously threatens the hydrological and ecological balance. The overexploitation is explained by the increase in water mineralization, which poses a risk of soil salinization. To this end, this article deals with the subject of current salinity and predict its evolution over time by means of the modeling of the artificial neural network (ANN), according to the period of low water and the period of high water. The ANN were trained using three different algorithms: the Scaled Conjugate Gradient back propagation (SCG) algorithm and One Step Secant back propagation (OSS) algorithm and Quasi-Newton algorithm (BFGS). The performance results indicate that the three algorithms provided satisfactory simulations according to the determination coefficient (R2) and the performance criteria of the mean square error (RMSE), with priority to the BFGS algorithm; where the coefficient of determination using the BFGS algorithm varies between 69.5% and 95.3%. The BFGS method presents better results in order to design appropriate institutional mechanisms, capable of leading to the protection of the quality of these resources essential to the promotion of sustainable development.http://fens.usv.ro/index.php/FENS/article/view/887/792outflowdrainagesalinityouedwater tableartificial neural networkinflow |
| spellingShingle | Zahra BOUHALI Larbi DJABRI Hamza BOUGUERRA Fatma TRABELSI Azzedine HANI Hicham CHAFFAI USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA) Food and Environment Safety outflow drainage salinity oued water table artificial neural network inflow |
| title | USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA) |
| title_full | USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA) |
| title_fullStr | USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA) |
| title_full_unstemmed | USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA) |
| title_short | USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA) |
| title_sort | use of artificial neural networks for modeling inflow and outflow and salinity of lake fetzara in the region annaba ne algeria |
| topic | outflow drainage salinity oued water table artificial neural network inflow |
| url | http://fens.usv.ro/index.php/FENS/article/view/887/792 |
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