Cyanobacteria bloom prediction model and parameters optimization based on genetic algorithm
蓝藻水华预报模型及基于遗传算法的参数优化

Cyanobacteria bloom prediction is very important for water crisis and water resource security. Technique of dynamic spatial environmental modelling is used to develop cyanobacteria bloom prediction model used in three bays (Meiliang Bay, Zhushan Bay, Gong Bay) of northern Taihu Lake. The initial model parameters are obtained from field observation. The four parameters highly sensitive in chlorophyll-a concentration prediction are determined using Genetic Algorithm optimization technique. The observed field data of water environment and meteorological conditions in Taihu Lake from April to September 2008 are used for this purpose. The results showed that, Genetic Algorithm is comprehensive and efficient in optimizing model parameters, thus effective in improving prediction accuracy of the model and the relative residual decreases.