Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary algorithms

Abstract In the context of this study two concepts were applied for the development of rule-based agents of algal populations: (1) rule discovery by means of a hybrid evolutionary algorithms (HEA) and rigorous k-fold cross-validation, and (2) rule generalisation by means of merged time-series data of lakes belonging to the same lake category. The rule-based agents developed during this study proved to be both explanatory and predictive. It has been demonstrated that the interpretation of the rules can be brought into the context of empirical and causal knowledge on chlorophyll-a dynamics as well as population dynamics of Microcystis and Oscillatoria under specific water quality conditions. The k-fold cross-validation of the agents based on measured data of each year of similar lakes revealed good forecasting accuracy resulting in r2 values ranging between 0.39 and 0.63.

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