Unravelling and forecasting algal population dynamics
in two lakes different in morphometry and
eutrophication by neural and evolutionary computation
Ecological Informatics, 1 (2):
133--151 (April 2006)DOI: doi:10.1016/j.ecoinf.2006.02.004
Abstract
Precious ecological information extracted from
limnological long-term time series advances the theory
on functioning and evolution of freshwater ecosystems.
This paper presents results of applications of
artificial neural networks (ANN) and evolutionary
algorithms (EA) for ordination, clustering, forecasting
and rule discovery of complex limnological time-series
data of two distinctively different lakes. Ten years of
data of the shallow and hypertrophic Lake Kasumigaura
(Japan) are used in comparison with 13 years of data of
the deep and mesotrophic Lake Soyang (Korea). Results
demonstrate the potential that: (1) recurrent
supervised ANN and EA facilitate 1-week-ahead
forecasting of outbreaks of harmful algae or water
quality changes, (2) EA discover explanatory rule sets
for timing and abundance of harmful outbreaks algal
populations, and (3) non-supervised ANN provide
clusters to unravel ecological relationships regarding
seasons, water quality ranges and long-term
environmental changes.