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

被引：0

作者：

Dawson, CW ^{[1
]}

See, LM ^{[1
]}

Abrahart, RJ ^{[1
]}

Wilby, RL ^{[1
]}

Shamseldin, AY ^{[1
]}

Anctil, F ^{[1
]}

Belbachir, AN ^{[1
]}

Bowden, G ^{[1
]}

Dandy, G ^{[1
]}

Lauzon, N ^{[1
]}

Maier, H ^{[1
]}

Mason, G ^{[1
]}

机构：

[1] Univ Loughborough, Dept Comp Sci, Loughborough, Leics, England

来源：

Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5 | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2666 / 2670

页数：5

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