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| Titre: | A hybrid model for modelling the salinity of the tafna river in Algeria |
| Auteur(s): | Houari, Khemissi
Hartani, Tarik
Remini, Boualem
Lefkir, Abdelouhab
Abda, Leila
Heddam, Salim |
| Mots-clés: | Adaptive-Network-Based Fuzzy Inference System (ANFIS)
Hybrid model
Neuro-fuzzy
Salinity
Salt flow
Tafna River |
| Date de publication: | 2019 |
| Collection/Numéro: | Journal of Water and Land Development/ Vol.40, N°1 (2019);pp. 127-135 |
| Résumé: | In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R 2 ), Nash-Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R 2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing). © 2019 Khemissi Houari et al |
| URI/URL: | DOI:10.2478/jwld-2019-0014
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6962 |
| ISSN: | 14297426
2083-4535 Electronic |
| Collection(s) : | Publications Internationales
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