%0 Journal Article %T 结合温度因子估算太湖叶绿素a含量的神经网络模型 %T The neural network model for estimation of chlorophyll-a with water temperature in Lake Taihu %A 孔维娟 %A 马荣华 %A 段洪涛 %A KONG,Weijuan %A MA,Ronghua %A DUAN,Hongtao %J 湖泊科学 %J Journal of Lake Sciences %@ 1003-5427 %V 21 %N 2 %D 2009 %P 193-198 %K 叶绿素a;BP神经网络;MODIS;水温;太湖 %K Chlorophyll-a;BP neural network;MODIS;water temperature;Lake Taihu %X 神经网络方法估算复杂水体水质参数的优越性已经得到证实.基于太湖水体实测叶绿素a浓度,利用MODIS250m影像和反演得到的水温数据建立了估算太湖水体叶绿素a含量的两个单隐层BP神经网络模型:NN1模型不含温度因子、NN2模型包含温度因子,采用Levenberg-Marquardt算法训练网络,利用初期终止方法提高网络泛化能力,均取得了较高估算精度,其中包含温度因子的反演模型精度稍有提高,但不显著. %X The advantage of neural network method for estimating water quality parameters of complex water body has been approved. Using in-situ measurement data of chlorophyll-a concentration, imageries of MODIS 250m and retrieval model of water temperature, we develop two single-hidden-layer BP neural network models for estimating chlorophyll-a in Lake Taihu: Model NN1 without temperature input and Model NN2 with temperature input. The training method is used by Levenberg-Marquardt algorithm, and the early-stage determinationin the modeling is used to improve generalization. The results show that: the estimation precision of the two models is high, in which the estimation precision of neural network input with temperature has been improved although the test is not significant. %R 10.18307/2009.0206 %U http://www.jlakes.org/ch/reader/view_abstract.aspx %1 JIS Version 3.0.0