Forecasting suspended sediment load using regularized neural network: Case study of the Isser River (Algeria)
| Autor: | Mohamed Remaoun, Lahbassi Ouerdachi, Oussama Derdous, Hamouda Boutaghane, Salah Eddine Tachi |
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| Rok vydání: | 2016 |
| Předmět: |
Hydrology
lcsh:TC401-506 Environmental Engineering Early stopping Artificial neural network business.industry 0208 environmental biotechnology Geography Planning and Development Sediment lcsh:River lake and water-supply engineering (General) 02 engineering and technology Development early stopping Agricultural and Biological Sciences (miscellaneous) Isser River 020801 environmental engineering Beni Amran reservoir Water resources Landscape architecture business sediment load artificial neural network Water Science and Technology |
| Zdroj: | Journal of Water and Land Development, Vol 29, Iss 1, Pp 75-81 (2016) |
| ISSN: | 2083-4535 |
| Popis: | In the management of water resources in different hydro-systems it is important to evaluate and predict the sediment load in rivers. It is difficult to obtain an effective and fast estimation of sediment load by artificial neural network without avoiding over-fitting of the training data. The present paper comprises the comparison of a multi-layer perception network once with non-regularized network and the other with regularized network using the Early Stopping technique to estimate and forecast suspended sediment load in the Isser River, upstream of Beni Amran reservoir, northern Algeria. The study was carried out on daily sediment discharge and water discharge data of 30 years (1971–2001). The results of the Back Propagation based models were evaluated in terms of the coefficient of determination (R2) and the root mean square error (RMSE). Results of the comparison indicate that the regularizing ANN using the Early Stopping technique to avoid over-fitting performs better than non-regularized networks, and show that the overtraining in the back propagation occurs because of the complexity of the data introduced to the network. |
| Databáze: | OpenAIRE |
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