基于BP神经网络的温榆河水质参数反演模型研究

为进一步提高内陆水体水质参数遥感反演的准确性，北京市温榆河被选为研究对象，研究选取ETM＋数据和准同步实测水质指标（浊度、BOD；）数据，建立了多个隐含层数目为1的BP神经网络模型，并选出分别针对浊度和BOD5的最佳神经网络模型，利用ETM＋影像的波段组合值反演了浊度和BOD，浓度值。最后将其反演结果与常规多元线性回归模型的反演结果进行精度比较。结果表明：温榆河的水质参数遥感反演为非线性问题，使用BP神经网络方法进行浊度与BOD，两种水质参数反演的结果优于线性回归方法的反演结果。

Study on retrieval model of water quality parameter in Wenyu River based on BP neural network

In order to further improve the accuracy of remote sensing retrieval of inland water quality, the paper chose Wenyu River in Beijing as research object,and used ETM ＋ data and plesioehronous meas- ured water quality parameters（ turbidity, BOD5 ） data to establish BP neural network models with several hidden layers being one. It chose the best neural network model aimed at turbidity and BOD5 and used ETM ＋ image, to retrieve turbidity values and BODsconcentration values. Finally, it compared the re- trieval results with the result retrieved by the conventional multiple linear regression model. The result shows that the remote sensing retrieval of Wenyu River water quality is a nonlinear problem, using BP neural network method for both turbidity and BOD5 water quality retrieval is superior to the linear regres- sion method.