Following a comparison of current alternative approaches for modelling and prediction of algal blooms, artificial neural networks are introduced and applied as a new, promising model type. The neural network applications were developed and validated by limnological time-series from four different freshwater systems. The water-specific time-series comprised cell numbers or biomass of the ten dominating algae species as observed over up to twelve years and the measured environmental driving variables. The resulting predictions on succession, timing and magnitudes of algal species indicate that artificial neural networks can fit the complexity and nonlinearity of ecological phenomena apparently to a high degree.
Ecological Modelling – Elsevier
Published: Mar 1, 1997
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