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Evaluation of artificial neural network techniques for river flow forecasting

Gabitsinashvili, George; Namgaladze, Dimitri and Bertacchi Uvo, Cintia LU (2007) 3rd International Conference on Environmental Engineering and Management In Environmental Engineering and Management Journal 6(1). p.37-43
Abstract
River runoff forecasting is one of the most complex areas of research in hydrology because of the uncertainty of hydrological and meteorological parameters and scarcity of adequate records. Artificial neural networks (ANN) can be an efficient way of modeling stream flow processes as it is capable of controlling and modelling nonlinear and complex systems and does not require describing the complex nature of the hydrological processes. In this study, daily river flow is forecasted using two ANN models: a Multi Layer Perceptron (MLP) network and a Radial Basis Function (RBF) Network. The ANN technique was applied to predict runoff in three mountain rivers in Georgia. The results show that ANNs can be successfully applied to forecast runoff... (More)
River runoff forecasting is one of the most complex areas of research in hydrology because of the uncertainty of hydrological and meteorological parameters and scarcity of adequate records. Artificial neural networks (ANN) can be an efficient way of modeling stream flow processes as it is capable of controlling and modelling nonlinear and complex systems and does not require describing the complex nature of the hydrological processes. In this study, daily river flow is forecasted using two ANN models: a Multi Layer Perceptron (MLP) network and a Radial Basis Function (RBF) Network. The ANN technique was applied to predict runoff in three mountain rivers in Georgia. The results show that ANNs can be successfully applied to forecast runoff using rainfall time series for the studied sub-catchments. A comparative study of both networks indicates that RBF models require little background knowledge of ANNs and need less time for development. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
radial basis function, modelling, rainfall-runoff, artificial neural network, multi layer perceptron, river flow forecasting
in
Environmental Engineering and Management Journal
volume
6
issue
1
pages
37 - 43
publisher
Gh. Asachi Technical University of Iasi, Romania
conference name
3rd International Conference on Environmental Engineering and Management
external identifiers
  • wos:000254459200007
ISSN
1843-3707
1582-9596
language
English
LU publication?
yes
id
56d5fa71-119b-4fd6-adf5-a866b6f48252 (old id 1407333)
date added to LUP
2009-06-02 14:24:47
date last changed
2016-04-15 19:42:45
@inproceedings{56d5fa71-119b-4fd6-adf5-a866b6f48252,
  abstract     = {River runoff forecasting is one of the most complex areas of research in hydrology because of the uncertainty of hydrological and meteorological parameters and scarcity of adequate records. Artificial neural networks (ANN) can be an efficient way of modeling stream flow processes as it is capable of controlling and modelling nonlinear and complex systems and does not require describing the complex nature of the hydrological processes. In this study, daily river flow is forecasted using two ANN models: a Multi Layer Perceptron (MLP) network and a Radial Basis Function (RBF) Network. The ANN technique was applied to predict runoff in three mountain rivers in Georgia. The results show that ANNs can be successfully applied to forecast runoff using rainfall time series for the studied sub-catchments. A comparative study of both networks indicates that RBF models require little background knowledge of ANNs and need less time for development.},
  author       = {Gabitsinashvili, George and Namgaladze, Dimitri and Bertacchi Uvo, Cintia},
  booktitle    = {Environmental Engineering and Management Journal},
  issn         = {1843-3707},
  keyword      = {radial basis function,modelling,rainfall-runoff,artificial neural network,multi layer perceptron,river flow forecasting},
  language     = {eng},
  number       = {1},
  pages        = {37--43},
  publisher    = {Gh. Asachi Technical University of Iasi, Romania},
  title        = {Evaluation of artificial neural network techniques for river flow forecasting},
  volume       = {6},
  year         = {2007},
}