Research Article of Global Journal of Artificial Intelligence
Neuro-Fuzzy Approach to River Sediment Yield Prediction
Balogun O. 1, Akinboro S. A2, Ogunseye, A. A3
1,2 College of Information and Communication Technology, Bells University of Technology, Otta, Nigeria; 3Department of Electronic & Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
This work is motivated by the critical role that sediment yield prediction plays in preventing natural and economic disasters. Methods based on regression techniques have been used to solve the problem but they are generally inadequate in predicting river sediment yield because of the inherent complexity of the problem. This work uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) to solve the problem. The ANFIS model accepts four input data namely temperature, rainfall, water stage and water discharge and gives on output data that represents the sediment yield. The ANFIS model was developed and simulated with MATLAB 7.0 using the Levenberg-Marquardt optimization method and trained with a maximum of 1500 epochs at a learning rate of 0.5. the results obtained was compared with the ones obtained with the Artificial Neural Network (ANN) model and it was found that the ANFIS model performs better than the ANN model.
Keywords: ANFIS, ANN, Neuro-Fuzzy, Backpropagation algorithm, Sediment yield.
How to cite this article:
Balogun O., Akinboro S. A, Ogunseye, A. A. Neuro-Fuzzy Approach to River Sediment Yield Prediction. Global Journal of Artificial Intelligence, 2019; 1:2.
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