A Comparison of MLR and Three Different Artificial Neural Networks Techniques for Daily Mean Flow Prediction
No Thumbnail Available
Date
2009
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
World Scientific and Engineering Acad and Soc
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
Prediction for hydrologic events has always been an important issue for optimizing and planning the whole system. In this study, a conventional multivariate linear regression method and three different ANN (Artificial Neural Networks) techniques (Feed Forward Back Propagation, Generalized Regression and Radial Basis Function) were used to predict and model daily mean flow of Anamur River. Because of the climate change, last 5234 days' data between the years 1989 and 2003 were processed and those three techniques of ANN and the conventional method MLR (multivariate linear regression) were compared to each other. The best results were obtained with FFBP algorithm.
Description
Keywords
ANN, MLR, daily flow, GRNN, FFBPNN, RBFNN
Turkish CoHE Thesis Center URL
Fields of Science
Citation
0
WoS Q
Scopus Q
Source
7th WSEAS International Conference on Environment, Ecosystems and Development -- DEC 14-16, 2009 -- Puerto de la Cruz, SPAIN
Volume
Issue
Start Page
61
End Page
+