Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/35881
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Type: Conference paper
Title: Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks
Author: Kingston, G.
Maier, H.
Lambert, M.
Citation: International Joint Conference on Neural Networks, 2006:pp.4870-4877
Publisher: IEEE
Publisher Place: CDROM
Issue Date: 2006
Series/Report no.: IEEE International Joint Conference on Neural Networks (IJCNN)
ISBN: 0780394909
9780780394902
ISSN: 1098-7576
Conference Name: International Joint Conference on Neural Networks (2006 : Vancouver, Canada)
Abstract: Cyanobacteria blooms are a major water quality problem in the River Murray and models are needed In provide warnings of such blooms and to investigate the response of cyanobacteria to different management strategies. However, the data, available this problem, are subject to considerable errors and consequently, it can be expected that the performance of any data-driven model will be limited. Two ANN models, developed using deterministic and Bayesian approaches, are compared to assess the strengths and limitations of these data-driven modelling approaches in the face of this data uncertainty. The resulting ANNs are assessed in terms of their usefulness as forecasting models and as tools for gaining information about the system.
Description: Copyright © 2006 IEEE
RMID: 0020062793
DOI: 10.1109/IJCNN.2006.247166
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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