Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.247166 | Vancouver, BC |
Keywords | Field | DocType |
Bayes methods,geophysics computing,hydrological techniques,microorganisms,neural nets,rivers,water resources,ANN models,Bayesian approach,River Murray,cyanobacteria blooms,cyanobacteria forecasting,data-driven model,deterministic artificial neural networks,water quality problem | Data mining,Computer science,Artificial intelligence,Water resources,Artificial neural network,Machine learning,Water quality,Bayesian probability | Conference |
ISSN | ISBN | Citations |
2161-4393 | 0-7803-9490-9 | 2 |
PageRank | References | Authors |
0.38 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Greer B. Kingston | 1 | 9 | 1.03 |
| Holger R. Maier | 2 | 738 | 72.97 |
| M. F. Lambert | 3 | 14 | 2.16 |