Feed-Forward Artificial Neural Network Model For Forecasting Rainfall Run-Off
Abstract
This paper presents the results of a blind test of the ability of a feed-forward artificial neural network to provide out-of-sample forecasting of rainfall run-off using real data. The results obtained are comparable with the results obtained using best methods currently available. The focus of the paper has been an easily repeatable experiment applied to rainfall and run-off data for a catchment area; which particular catchment was not revealed to the experimenters, i.e. a blind experiment. To this end, a simple model has been specified, and the architecture of the neural network and the data preparation procedures adopted ...
View more >This paper presents the results of a blind test of the ability of a feed-forward artificial neural network to provide out-of-sample forecasting of rainfall run-off using real data. The results obtained are comparable with the results obtained using best methods currently available. The focus of the paper has been an easily repeatable experiment applied to rainfall and run-off data for a catchment area; which particular catchment was not revealed to the experimenters, i.e. a blind experiment. To this end, a simple model has been specified, and the architecture of the neural network and the data preparation procedures adopted are discussed in detail. The results are presented and discussed in detail and the extent to which the system was found to be non-linear is quantified.
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View more >This paper presents the results of a blind test of the ability of a feed-forward artificial neural network to provide out-of-sample forecasting of rainfall run-off using real data. The results obtained are comparable with the results obtained using best methods currently available. The focus of the paper has been an easily repeatable experiment applied to rainfall and run-off data for a catchment area; which particular catchment was not revealed to the experimenters, i.e. a blind experiment. To this end, a simple model has been specified, and the architecture of the neural network and the data preparation procedures adopted are discussed in detail. The results are presented and discussed in detail and the extent to which the system was found to be non-linear is quantified.
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Journal Title
Environmetrics
Volume
9
Subject
Mathematical sciences
Environmental sciences