Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation

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 utilized 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.