Paper Title :The Efficiency of Using Different of Learning Algorithms in Artificial Neural Network Model For Flood Forecasting at Upper River Ping Catchment, Thailand
Author :Tawee Chaipimonplin
Article Citation :Tawee Chaipimonplin ,
(2017 ) " The Efficiency of Using Different of Learning Algorithms in Artificial Neural Network Model For Flood Forecasting at Upper River Ping Catchment, Thailand " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 40-44,
Volume-5, Issue-1
Abstract : This paper presents the result of exploration the efficiency of 12 learning algorithms; Levenberg-Marquardt (LM),
Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backprogagation (RP), Scaled Conjugate Gradient
(SCG), Conjugate Gradient with Powell/Beale Restarts (CGB), Fletcher-Powell Conjugate Gradient (CGF), Polak-Ribiere
Conjugate Gradient (CGP), One Step Secant (OSS), Variable Learning Rate Gradient Descent (GDX), Gradien Descent with
Momentum (GDM), Gradient Descent (GD) in artificial neural network model by forecast flood at 6 and 12 hour in
advances. In addition, to compare the algorithms performance, different number of hidden nodes by 1, 50%, 75% and 100%
of the number of input variables and selecting input variables with different input determination techniques; Cross
correlation (C), Stepwise regression (S), Genetic algorithms (G) and combination between C and S (CS) are included in this
study.In conclusion, LM and BFG are the best algorithm for flood forecasting at 6 butfor 12 hour is only BFG with different
input variables and number of hidden nodes as the maximum of R2
value are 0.99 and 0.97 respectively.
Index terms- Artificial neural network, Upper River Ping, Flood forecasting, Thailand
Type : Research paper
Published : Volume-5, Issue-1
Copyright: © Institute of Research and Journals
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Published on 2017-03-07 |
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