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.

Introduction

Predictive agents for specific algal populations can be powerful tools for early warning and operational control of harmful algal blooms in lakes and drinking water reservoirs. One way of developing predictive agents for algal populations is the extraction of generic rules from ecological time-series data by means of evolutionary algorithms as suggested by Recknagel (2003).

This study demonstrates the development of rule-based agents from merged time-series data of lakes belonging to the same lake category by means of a hybrid evolutionary algorithm (HEA) and a rigorous k-fold cross-validation framework.

Three rule-based agents will be discussed and validated which are applicable for forecasting 5 to 7-days-ahead the concentration of chlorophyll-a in the warm monomictic and mesotrophic reservoirs Myponga and Happy Valley (Australia), the abundance of Microcystis in the shallow polymictic hypertrophic lakes Kasumigaura and Suwa (Japan) as well as the abundance of Oscillatoria in the shallow polymictic and hypertrophic lakes Veluwemeer and Wolderwijd (The Netherlands).

The resulting rule-based agents 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 understanding of 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 two similar lakes revealed good forecasting accuracy resulting in r² values ranging between 0.39 and 0.63.