Neuro-Fuzzy Approach to River Sediment Yield Prediction


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


Global Journal of Artificial Intelligence

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.

Free Full-text PDF


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.


References:

1. Agrawal Samarth, Manoj Jindal, Pillai, G.N (2010). Momentum Analysis based Stock Market Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS).www.iaeng.org/publication/IMECS2010/IMECS2010_pp526-531.pdf. Date of last visit:10/12/2015
2. Bisht Dinesh C.S., and Ashok Jangid (2011) Discharge modelling using Adaptive Neuro-Fuzzy Inference System. International Journal of Advance Science and Technology Vol. 31, 1-5
3. Firat M. (2008). Comparison of Artificial Intelligence Techniques for river flow Forecasting. Hydrological and Earth system Sciences Journal.,12, 123-139.
4. Johnson, D.L., Ambrose, S.H., Bassett, T.J., Bowen, M.L., Crummey, D.E., Isaacson, J.S., Johnson, D.N., Lamb, P., Saul, M., and Winter-Nelson, A.E. (1997). Meaning of Environmental terms. Journal of environmental Quality. 26: 581-589.
5. Lohani, A.K., Goel, N.K., Bhatia, K.K.S (2009). Deriving stage discharge sediment concentration relationships using fuzzy logic. Hydrological Science journal 52(4), 793-807.
6. Nagy H., Watanabe, K., and Hirano, M. (2002). Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model. Journal of Hydrological Engineering, 128(6), 588–595.
7. Ozgur Kisi, TefarukHaktanir, Mehmet Ardiclioglu, Ozgur Ozturk, EkremYalsin, and Salih Uludag (2008). Adaptive Neuro-fuzzy computing technique for suspended sediment estimation. Advances in Engineering Software, Volume 40, issue 6, 438-444
8. Pankaj Kumar (2011) Crop yield forecasting by ANFIS. Mathematical theory and modeling journal Vol. 1 No. 3, ISSN 2225-0522, 56
9. Pollution issues (2016). Sedimentation. www.pollutionissues .com/Re-Sy/sedimentation.html. Date of last visit: 14/05/16
10. Raghuwanshi, N., Singh, R., and Reddy, L. (2006). Runoff and Sediment Yield Modeling Using Artificial Neural Networks: Upper Siwane River, India. J. Hydrol. Eng., 11(1), 71–79.
11. Ogundoyin I.K et al.,(2009). Artificial Neural Network Approach to River sediment yield prediction: A case study of Ogun-Osun River Basin: A case sudy of Ogun-Osun River Basin. Joural of Computer science and its applications, pp.8.