Modelling Microcystis aeruginosa bloom dynamics in the Nakdong River by means of evolutionary computation and statistical approach

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Abstract

Dynamics of a bloom-forming cyanobacteria (Microcystis aeruginosa) in a eutrophic river–reservoir hybrid system were modelled using a genetic programming (GP) algorithm and multivariate linear regression (MLR). The lower Nakdong River has been influenced by cultural eutrophication since construction of an estuarine barrage in 1987. During 1994–1998, the average concentrations of nutrients and phytoplankton were: NO3–N, 2.7 mg l−1; NH4+–N, 0.6 mg l−1; PO43−–P, 34.7 μg l−1; and chlorophyll a, 50.2 μg l−1. Blooms of M. aeruginosa occurred in summers when there were droughts. Using data from 1995 to 1998, GP and MLR were used to construct equation models for predicting the occurrence of M. aeruginosa. Validation of the model was done using data from 1994, a year when there were severe summer blooms. GP model was very successful in predicting the temporal dynamics and magnitude of blooms while MLR resulted rather insufficient predictability. The lower Nakdong River exhibits reservoir-like ecological dynamics rather than riverine, and for this reason a previous river mechanistic model failed to describe uncertainty and complexity. Results of this study suggest that an inductive-empirical approach is more suitable for modelling the dynamics of bloom-forming algal species in a river–reservoir transitional system.

Introduction

A comprehensive understanding of ecosystem dynamics requires numerous approaches. Ecological modelling is a good solution for this purpose when it contains adequate data and expressions for the system of interest (Straskraba, 1994). To interpret data or forecast ecological conditions, researchers traditionally select deductive mathematical or statistical models. Due to the difficulties involved in solving complex interactions among diverse variables and parameters, sophisticated machine learning techniques [e.g. artificial neural networks (ANNs) and evolutionary computation (EC)] recently have been applied in ecological modelling (Lek et al., 1996; Recknagel, 1997; Fielding, 1999; Whigham, 2000).

Genetic programming (GP) is a technique derived from evolutionary computation, originally based on evolving variable-length LISP programs (Koza, 1992). GP is a population-based search that evolves tree-like structures that represent functions, equations, or programs. GP performs generational evolution of the solution candidates to find the best solution for a certain problem from the solution space (Banzhaf et al., 1998). This is achieved by applying various search operators such as crossover (swapping sub-trees between parents) and mutation (randomly recreating a subtree of a parent). The inductive stochastic approach of GP, combined with few assumptions regarding the form or limitations of developed models, shows some advantages over other approaches in modelling freshwater ecosystems.

Of the various freshwater resources, river systems are particularly impacted by human activities, frequently displaying cultural eutrophication. This sometimes is exacerbated by regulation of water flow (Moss, 1998). Extended retention time as well as excessive nutrient loads can result in severe blooms of blue–green algae in rivers. Algal blooms are stimulated by various circumstances so that it is difficult to develop a fixed model, which considers all possible situations (Recknagel, 1997; Jeong, 2000). Genetic programming can search for suitable variables as well as their interactions by evaluating the underlying data for significant patterns. This allows models to be developed that consider the behavior of algal blooms in each specific environment.

The lower Nakdong River has exhibited severe blue–green algal blooms in hot summer months (Ha, 1999). Acceleration of eutrophication due to the construction of the barrage at the river mouth, coupled with high nutrient loads caused this situation (Joo et al., 1997). Some modelling efforts have attempted to predict the blooms, but they considered mainly water quality, and did not provide an ecosystem perspective in this river.

In this study, time-series dynamics of Microcystis aeruginosa blooms were modelled using an extended GP technique, specifically designed for time-series models. The empirical database from the lower Nakdong River was used to evolve the best model to predict the time-series changes of M. aeruginosa. By varying model inputs in a forecasting mode, this model can be used in water quality and ecosystem management applications. To compare the capability of GP modelling, multivariate linear regression (MLR) model was also constructed, and time-series prediction between them was evaluated. The present study provides a good example of application of GP to a river–reservoir hybrid system.

Section snippets

Description of the study site

The Nakdong River basin is situated in the monsoon climate of South Korea (3537° N, 127–129° E) (Fig. 1). South Korea experiences four distinct seasons, and is characterized by heavy rainfall during the monsoon period and several typhoon events. The annual mean rainfall across the Nakdong River basin is about 1200 mm, and more than 50% of the total amount is concentrated during the hot summer months (June–August). The annual mean water temperature at the study site was 13.7 °C. The mean water

Limnological data collection

Precipitation data were obtained from five representative meteorological stations within the Nakdong River basin (Andong, Daegu, Hapchun, Jinju, and Miryang) from 1994 to 1998. River flow data were obtained from the Flood Control Center. Irradiance, wind velocity, and evaporation data were collected from the Busan Meteorological Station, which is the nearest station to the study site.

Weekly water samples were collected at 0.5 m depth at the river site, and the following limnological parameters

Limnological aspects

Most limnological data from the lower Nakdong River exhibited distinct inter-annual variability (Table 1). Most physico-chemical parameters were related to rainfall amount in a certain year except Secchi depth and DO. For example, water temperature, turbidity, conductivity, alkalinity, and nutrient concentrations varied according to the fluctuation of total annual rainfall. In the case of turbidity, a high value was observed in both 1994 and 1998, when there was algal proliferation and high

Limnology of the lower Nakdong River

The lower Nakdong River displayed reservoir-like characteristics, and its nutrient and chlorophyll a concentrations indicated a eutrophic situation according to Wetzel (1983). River eutrophication is largely influenced by flow regulation and nutrient loadings (Webb and Walling, 1992). Joo et al. (1997) suggested that eutrophication in the lower Nakdong River was mainly due to the regulation of water flow. The construction of an estuarine barrage in 1987 had a synergistic effect with nutrient

Conclusion

Mining dynamic and complicated ecological data require a suitable methodology to obtain a satisfactory result. The increasing amount of data that has become available today for aquatic ecosystems can support the basic requirements of machine learning techniques. Models derived from such techniques can address the changing environments that occur due to human intervention. Machine learning approaches such as GP are good tools for this purpose, and suitable ecological models derived from these

Acknowledgements

The authors are grateful to Dr. H.W. Kim of Sunchon National University, and Dr. K. Ha of Pusan National University (PNU) for providing plankton community data. We also thank Mr. S.B. Park, Mr. J.S. Kim, and Mr. J.G. Kim of PNU for assistance in the field. We also are indebted to Dr. Friedrich Recknagel of the University of Adelaide for paying warm attention during the preparation of this article. This study was financially supported by the Institute of Environmental Technology and Industry

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