Monthly runoff forecasting by means of artificial neural networks (ANNs)

Authors

  • A. Kalteh Department of Range and Watershed Management, Faculty of Natural Resources, University of Guilan, Somehsara, Iran
  • P. Hjorth Department of Water Resources Engineering, Lund University, Box 118, SE-22 100, Lund, Sweden
Abstract:

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at decomposing the process into different clusters based onself-organizing map (SOM) ANN approach, and thereafter modelling different clusters into outputs using separatefeed-forward multilayer perceptron (MLP) and supervised self-organizing map (SSOM) ANN models. Specifically,three different SOM models have been employed in order to cluster the input patterns into two, three, and fourclusters respectively so that each cluster in each model corresponds to certain physics of the process underinvestigation and thereafter modelling of the input patterns in each cluster into corresponding outputs using feedforwardMLP and SSOM ANN models. The employed models were developed on two different watersheds, Iranianand Canadian. It was found that although the idea of decomposition based on SOM is highly persuasive, our resultsindicate that there is a need for more principled procedure in order to decompose the process. Moreover, according tothe modelling results the SSOM can be considered as an alternative approach to the feed-forward MLP.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

monthly runoff forecasting by means of artificial neural networks (anns)

over the last decade or so, artificial neural networks (anns) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. however, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at de...

full text

monthly runoff estimation using artificial neural networks

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

full text

Rainfall - Runoff Modelling Using Artificial Neural Networks ( ANNs )

Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowle...

full text

Short-term wind forecasting using artificial neural networks (ANNs)

The integration of wind farms in power networks has become an important problem. As electricity cannot be preserved because of the highest cost of storage, electricity production must following market demand, necessarily. Short-long term wind forecasting over different time steps is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based on ...

full text

Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding

In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 13  issue 2

pages  181- 191

publication date 2008-12-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023