A hybrid neural–genetic algorithm for reservoir water quality management

Abstract

A combined neural network and genetic algorithm (GA) was developed for water quality management of Feitsui Reservoir in Taiwan. First, an artificial neural network (ANN) model was employed to simulate the behavior of nutrient loads into the reservoir. The data from watershed loads, precipitation in the watershed, and outflow were used in the ANN model to forecast the total phosphorus concentration in the reservoir. A 6-year (1992–97) record of water quality data was used for network training, and additional data collected in 1998–2000 were used for model verification. Further, a GA was used with this ANN model to optimize the control of nutrient loads from the watershed. The GA was used as a search strategy to determine the proper reduction rates of nutrient loads from the watershed so that the objective function could be as close to the optimal value as possible. The study results indicate that the ANN model can effectively simulate the dynamics of reservoir water quality. The GA is able to identify control schemes that reduce the in-reservoir total phosphorus concentration by as much as 60%, and water quality in the reservoir can be expected to achieve an oligotrophic (most of the time) or mesotrophic level if the watershed nutrient loads are reduced by 10–80%.