Knowledge discovery for prediction and explanation of blue-green algal dynamics in lakes by evolutionary algorithms

This paper applies an evolutionary algorithm to the problem of knowledge discovery on blue-green algae dynamics in a hypertrophic lake. Patterns in chemical and physical parameters of the lake and corresponding presence or absence of highly abundant blue-green algae species such as Microcystis spp, Oscillatoria spp and Phormidium spp are discovered by the machine learning algorithm. Learnt patterns are represented explicitly as classification rules, which allow their underlying hypothesis to be examined. Models are developed for the filamentous blue-green algae Oscillatoria spp and Phormidium spp, and the colonial blue-green algae Microcystis spp. Hypothesized environmental conditions which favour blooms of the three species are contrasted and examined. The models are evaluated on independent test data to demonstrate that models can be evolved which differentiate algae species on the basis of the environmental attributes provided.