Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks

Greer B. Kingston; Holger R. Maier; Martin F. Lambert

doi:10.1109/ijcnn.2006.247166

Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks

Greer B. Kingston, Holger R. Maier, Martin F. Lambert

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Cyanobacteria blooms are a major water quality problem in the River Murray and models are needed to provide warnings of such blooms and to investigate the response of cyanobacteria to different management strategies. However, the data available for 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.

Original language	English
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4870-4877
Number of pages	8
ISBN (Print)	0780394909, 9780780394902
DOIs	https://doi.org/10.1109/ijcnn.2006.247166
Publication status	Published or Issued - 2006
Externally published	Yes
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Conference

Conference	International Joint Conference on Neural Networks 2006, IJCNN '06
Country/Territory	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

ASJC Scopus subject areas

Software

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10.1109/ijcnn.2006.247166

Cite this

Kingston, G. B., Maier, H. R., & Lambert, M. F. (2006). Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 4870-4877). Article 1716776 (IEEE International Conference on Neural Networks - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ijcnn.2006.247166

@inproceedings{24d8b5c45a28437ba8966a19193575f6,

title = "Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks",

abstract = "Cyanobacteria blooms are a major water quality problem in the River Murray and models are needed to provide warnings of such blooms and to investigate the response of cyanobacteria to different management strategies. However, the data available for 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.",

author = "Kingston, \{Greer B.\} and Maier, \{Holger R.\} and Lambert, \{Martin F.\}",

year = "2006",

doi = "10.1109/ijcnn.2006.247166",

language = "English",

isbn = "0780394909",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4870--4877",

booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",

address = "United States",

note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

}

Kingston, GB, Maier, HR & Lambert, MF 2006, Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks. in International Joint Conference on Neural Networks 2006, IJCNN '06., 1716776, IEEE International Conference on Neural Networks - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 4870-4877, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canada, 16/07/06. https://doi.org/10.1109/ijcnn.2006.247166

Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks. / Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.
International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc., 2006. p. 4870-4877 1716776 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Maier, Holger R.

AU - Lambert, Martin F.

PY - 2006

Y1 - 2006

N2 - Cyanobacteria blooms are a major water quality problem in the River Murray and models are needed to provide warnings of such blooms and to investigate the response of cyanobacteria to different management strategies. However, the data available for 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.

AB - Cyanobacteria blooms are a major water quality problem in the River Murray and models are needed to provide warnings of such blooms and to investigate the response of cyanobacteria to different management strategies. However, the data available for 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.

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DO - 10.1109/ijcnn.2006.247166

M3 - Conference contribution

AN - SCOPUS:40649108316

SN - 0780394909

SN - 9780780394902

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

SP - 4870

EP - 4877

BT - International Joint Conference on Neural Networks 2006, IJCNN '06

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

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ER -

Kingston GB, Maier HR, Lambert MF. Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks. In International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc. 2006. p. 4870-4877. 1716776. (IEEE International Conference on Neural Networks - Conference Proceedings). doi: 10.1109/ijcnn.2006.247166

Forecasting cyanobacteria with Bayesian and deterministic artificial neural networks

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