ABSTRACT
In the regulated Nakdong River, algal proliferations are annually observed in some seasons, with cyanobacteria [Microcystis aeruginosa] appearing in summer and diatom blooms [Stephanodiscus hantzschii] in winter. This study aims to develop two ecological models forecasting future chlorophyll a at two time-steps [one-week and one-year forecasts], using recurrent neural networks tuned by genetic algorithm [GA-RNN]. A moving average [MA] method pre-processes the data for both short- and long-term forecasting to evaluate the effect of noise downscaling on model predictability and to estimate its usefulness and trend prediction for management purposes. Twenty-five physicochemical and biological components [e.g. water temperature, DO, pH, dams discharge, river flow, rainfall, zooplankton abundance, nutrient concentration, etc. from 1994 to 2006] are used as input variables to predict chlorophyll a GA-RNN models show a satisfactory level of performance for both predictions. Using genetic operations in the network training enables us to avoid numerous trial-and-error model constructions. MA-smoothed data improves the predictivity of models by removing residuals in the data prediction and enhancing the trend of time-series patterns. The results demonstrate efficient development of ecological models through selecting appropriate network structures. Data pre-processing with MA helps in forecasting long-term seasonality and trend of chlorophyll alpha, an important outcome for decision makers because it provides more reaction time to establish and control management strategies