Open AccessASSESSMENT OF THE ARTIFICIAL NEURAL NETWORKS TO GEOMORPHIC MODELLING OF SEDIMENT YIELD FOR UNGAUGED CATCHMENTS, ALGERIA
Author(s) -
Kamel Khanchoul,
Mahmoud Tourki,
Yves Le Bissonnais
Publication year - 2015
Publication title -
journal of urban and environmental engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.187
H-Index - 13
ISSN - 1982-3932
DOI - 10.4090/juee.2014.v8n2.175185
Subject(s) - sediment , hydrology (agriculture) , artificial neural network , surface runoff , mean squared error , erosion , environmental science , backpropagation , multilayer perceptron , sediment transport , yield (engineering) , soil science , geology , statistics , computer science , machine learning , geotechnical engineering , mathematics , geomorphology , ecology , biology , materials science , metallurgy
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.