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引用本文:孔维娟,马荣华,段洪涛.结合温度因子估算太湖叶绿素a含量的神经网络模型.湖泊科学,2009,21(2):193-198. DOI:10.18307/2009.0206
KONG Weijuan,MA Ronghua,DUAN Hongtao.The neural network model for estimation of chlorophyll-a with water temperature in Lake Taihu. J. Lake Sci.2009,21(2):193-198. DOI:10.18307/2009.0206
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结合温度因子估算太湖叶绿素a含量的神经网络模型
孔维娟1,2, 马荣华1, 段洪涛1
1.中国科学院南京地理与湖泊研究所, 南京 210008;2.南京大学地理信息科学系, 南京 210093
摘要:
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.
关键词:  叶绿素a  BP神经网络  MODIS  水温  太湖
DOI:10.18307/2009.0206
分类号:
基金项目:国家自然科学基金(40871168、40671138、40801137);国家科技支撑项目(2007BAC26B01)联合资助
The neural network model for estimation of chlorophyll-a with water temperature in Lake Taihu
KONG Weijuan1,2, MA Ronghua1, DUAN Hongtao1
1.Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.Department of Geography Information Science, Nanjing University, Nanjing 210093, P. R. China
Abstract:
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.
Key words:  Chlorophyll-a  BP neural network  MODIS  water temperature  Lake Taihu
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