Affiliation:
1. SOA University
2. Veer Surendra Sai University of Technology
3. IIT
Abstract
Artificial Neural Network (ANN) model is used to predict the suspended sediment load for the survey data collected on daily basis in the river Mahanadi. Genetic algorithm has been used to find the optimal level of process parameters such as water discharge and temperature for a minimum sedimentation load condition. Optimal level of process parameters obtained from the GA has been used in a trained neural network to obtain the sedimentation load condition. A comparative analysis is then made between GA and ANN for achieving minimum sedimentation load with the given process parameters.
Publisher
Trans Tech Publications, Ltd.
Reference10 articles.
1. S.Y. Liong, W.H. Lim and G. Paudyal, Real time river stage forecast for flood Blangladesh: neural network approach, Journal of computing in civil engineering ASCE 14(1), 1- 8, (1999).
2. A.E. N Elshafie, M.R. Taha and H. Basri, Neural network model for Nile River inflow forecasting analysis of historical inflow data,. Journal of Applied Sciences 8 (24), 4487-4499, (2008).
3. O. Makarynskyy, Improving wave prediction with artificial neural network,. Ocean Engineering 31, 709-724, (2004).
4. J.P. Grubert, Acid deposition in the eastern United States and neural network predictions for the future, Journal of Environmental Engineering and Science, 2 (2), 99-109, (2003).
5. A. Najah, A. Elshafie, O.A. Karim and O. Jaffar, Prediction of Johor River Water Quality Parameters Using Artificial Neural Networks, European Journal of Scientific Research, 28 (3), 422-435, (2009).
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献