ASSESSMENT OF THE ARTIFICIAL NEURAL NETWORKS TO ‎GEOMORPHIC MODELLING OF SEDIMENT YIELD FOR ‎UNGAUGED CATCHMENTS, ALGERIA

Authors

  • Kamel Khanchoul Badji Mokhtar University-Annaba
  • Mahmoud Tourki University of Mila
  • Yves Le Bissonnais Laboratoire d'étude des Interactions - Sol - Agrosystème - Hydrosystème (LISAH)

DOI:

https://doi.org/10.4090/juee.2014.v8n2.175-185

Keywords:

ANNs, geomorphology, sediment yield, modelling, ‎prediction, catchments

Abstract

Knowledge of sediment yield and the factors controlling it provides useful ‎information for estimating ‎erosion intensities within river basins. The objective of ‎this study was to build a model from which ‎suspended sediment yield could be ‎estimated from ungauged rivers using computed sediment yield and ‎physical ‎factors. Researchers working on suspended sediment transported by wadis in the ‎Maghreb are ‎usually facing the lack of available data for such river types. Further ‎study of the prediction of sediment ‎transport in these regions and its variability is ‎clearly required. In this work, ANNs were built between ‎sediment yield ‎established from longterm measurement series at gauging stations in Algerian ‎catchments and ‎corresponding basic physiographic parameters such as rainfall, ‎runoff, lithology index, coefficient of ‎torrentiality, and basin area. The proposed ‎Levenberg-Marquardt and Multilayer Perceptron algorithms to ‎train the neural ‎networks of the current research study was based on the feed-forward ‎backpropagation ‎method with combinations of number of neurons in each hidden ‎layer, transfer function, error goal. ‎Additionally, three statistical measurements, ‎namely the ‎root mean square error (RMSE), ‎the coefficient of ‎determination (R²), ‎and the efficiency factor (EF)‎ have been reported for ‎examining the forecasting ‎‎accuracy of the developed model.‎ Single plot displays of network outputs with ‎respect to targets for training ‎have provided good performance results and good ‎fitting . Thus, ANNs were a promising method for ‎predicting suspended sediment ‎yield in ungauged Algerian catchments.‎

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Author Biographies

Kamel Khanchoul, Badji Mokhtar University-Annaba

Department of Geology; Professor

Mahmoud Tourki, University of Mila

‎Department of Science and technology

Yves Le Bissonnais, Laboratoire d'étude des Interactions - Sol - Agrosystème - Hydrosystème (LISAH)

LISAH, Montpellier

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Published

2015-06-09

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Section

Articles