摘要
城市浅型湖泊治理是城市生态文明建设的重要组成部分。通过对黄石磁湖的IKONOS遥感影像进行预处理,建立了水质参数与卫星波段的多元线性回归模型、BP神经网络模型和RBF神经网络模型。通过比较不同模型的结果,运用可靠模型对整个湖体的COD、NH3-N、TN、TP指标进行反演。结果表明,神经网络模型对于磁湖水质指标的反演结果显著优于多元线性回归模型,其中BP神经网络模型对NH3-N、TP的模拟效果好,RBF神经网络模型对COD、TN的模拟效果较好。
The management of urban shallow lakes plays an important role in the urban ecological civilization construction.Using the IKONOS remote sensing image,two artificial neural network models,based on the BP（Back Propagation）and RBF（Radical Basis Function）,were set up to inverse the COD,NH3-N,TN and TP quality conditions of the Cihu Lake.The proposed models were also compared with the multivariate linear regression model.The results indicated that the model efficiency of the two ANN models were significantly higher than the multiple linear regression model.The BP model fitted the observed data better in the simulation of the NH3-N,TP,while the RBF neural network showed advantages in the simulation of the COD and TN.
出处
《南水北调与水利科技》
CAS
CSCD
北大核心
2016年第2期26-31,共6页
South-to-North Water Transfers and Water Science & Technology
基金
国家自然科学基金青年科学基金资助项目(51309009
51209162)~~
关键词
磁湖
遥感
神经网络
线性回归
水质反演
Cihu Lake
remote sensing
artificial neural network
linear regression
water quality inversion
