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 proceedingConference contributionpeer-review

3 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 languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4870-4877
Number of pages8
ISBN (Print)0780394909, 9780780394902
DOIs
Publication statusPublished or Issued - 1 Jan 2006
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

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

Conference

ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period16/07/0621/07/06

ASJC Scopus subject areas

  • Software

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