A comparative study of artificial neural network techniques for river stage forecasting

Although artificial neural networks have been applied to problems within hydrology for over ten years, there is little consensus on the 'best' type of neural network model to use and the most effective means of training the chosen model. In order to explore the different approaches neural...

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Bibliographic Details
Main Authors: Dawson, C.W., See, L.M., Abrahart, R.J., Wilby, R.L., Shamseldin, A.Y., Anctil, F., Belbachir, A.N., Bowden, G., Dandy, G., Lauzon, N., Maier, H.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Calibration
Civil engineering
Computer science
Geography
Hydrology
Predictive models
Rivers
Technology forecasting
Testing
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Summary:Although artificial neural networks have been applied to problems within hydrology for over ten years, there is little consensus on the 'best' type of neural network model to use and the most effective means of training the chosen model. In order to explore the different approaches neural network modellers use to forecasting river stage, an international comparison study was undertaken during 2004. This research was based on a set of rainfall and river stage data covering three winter periods for an unidentified river basin in England (with a catchment of 331,500 Ha in the north of the country), sampled at 15 minute intervals. Several neural network enthusiasts took part in the study from a number of different countries. The preferred methodologies and forecasting outputs from a number of 'blind' models of river stage developed by the participants have been collated and are presented in this paper.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2005.1556324