%0 Journal Article
%T Cyanobacteria bloom prediction model and parameters optimization based on genetic algorithm<br>蓝藻水华预报模型及基于遗传算法的参数优化
%A HUANG Jiacong
%A WU Xiaodong
%A GAO Junfeng
%A KONG Fanxiang Nanjing Institute of Geography
%A Limnology
%A Chinese Academy of Sciences
%A Nangjing
%A China Graduate University of Chinese Academy of Sciences
%A Beijing
%A China
%A <br>黄佳聪
%A 吴晓东
%A 高俊峰
%A 孔繁翔
%J 生态学报
%D 2010
%I 
%X 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.
%K cyanobacteria bloom
%K prediction model
%K dynamic spatial environmental modelling
%K parameter optimization
%K Genetic Algorithm
%K Taihu Lake<br>蓝藻水华
%K 预报模型
%K 动态空间环境模拟
%K 参数优化
%K 遗传算法
%K 太湖
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=FE163E5DB2274E5937319DE98913EC37&aid=D97FC9C397A4EFC8AEFD1EE80FD1E54C&yid=140ECF96957D60B2&vid=340AC2BF8E7AB4FD&iid=E158A972A605785F&sid=A8809BCBCBE59B72&eid=BF1CF7F9466F9169&journal_id=1000-0933&journal_name=生态学报&referenced_num=2&reference_num=25