The use of artificial neural networks (ANNs) for modelling the incidence of cyanobacteria in rivers was investigated by forecasting the occurrence of a species group of Anabaena in the River Murray at Morgan, Australia. The networks of backpropagation type were trained on 7 years of weekly data for eight variables, and their ability to provide a 4-week forecast was evaluated for a 28-week period. They were relatively successful in providing a good forecast of both the incidence and magnitude of a growth peak of the cyanobacteria within the limits required for water quality monitoring. The use of lagged versus unlagged inputs was evaluated in the implementation and performance of the networks. Lagged inputs proved far superior in providing a fit to the actual data. Sensitivity analysis of input variables was performed to evaluate their relative significance in determining the forecast values. The analysis indicated that for this data set for the River Murray, flow and temperature were the predominant variables in determining the onset and duration of cyanobacterial growth. Water colour was the next most important variable in determining the magnitude of the population growth peak. Plant nutrients nitrogen, phosphorus and iron, and turbidity were less important for this time period.
Ecological Modelling – Elsevier
Published: Jan 1, 1998
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera