Table of Contents
Journal of Computational Environmental Sciences
Volume 2014, Article ID 290127, 6 pages
http://dx.doi.org/10.1155/2014/290127
Research Article

Comparison of Back Propagation Neural Network and Genetic Algorithm Neural Network for Stream Flow Prediction

Department of Applied Mechanics and Hydraulics, NITK Surathkal, Mangalore 575025, India

Received 10 July 2014; Revised 7 August 2014; Accepted 11 August 2014; Published 28 August 2014

Academic Editor: Alberto Campisano

Copyright © 2014 C. Chandre Gowda and S. G. Mayya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Comparison of stream flow prediction models has been presented. Stream flow prediction model was developed using typical back propagation neural network (BPNN) and genetic algorithm coupled with neural network (GANN). The study uses daily data from Nethravathi River basin (Karnataka, India). The study demonstrates the prediction ability of GANN. The statistical tests show that GANN model performs much better when compared to BPNN model.