Water quality testing is of great significance to water ecological pollution control and environmental protec-tion.Taking Yuehai Lake Wetland in Yinchuan City as the research object,based on the Landsat-8 image data and the measured values of sampling water quality parameters,a convolutional neural network inversion model was established which include 8 input nodes,2 convolutional layer,16-1 normalization layer,16-8-1 the fully connected layer,1 pooling layer and 1 regression output layer.The concentrations of four water quality parameters,such as chemical oxygen de-mand(COD),ammonia nitrogen(NH3-N),total phosphorus(TP)and total nitrogen(TN),are inverted.Secondly,different convolution kernels,fully connected layers and optimizers were selected for comparative experiments.The re-sults show that:the convolution model 14 is suitable for the inversion of water quality indexes with COD concentration values>3 mg/L,and the coefficient of determination R2 is 0.91;the convolution model 10 is suitable for the inversion of water quality indicators with concentration values of ammonia nitrogen,total phosphorus and total nitrogen within 3 mg/L,and the coefficients of determination R2 are 0.95,0.81,and 0.9,respectively;The inversion accuracy(R2,RRMSE)of the convolution inversion model reached(0.91,4.44),which was better than the BP neural network and the traditional water quality inversion method.Finally,the data of COD was visualized,and a heat map of COD concentration value predicted by convolutional neural network was produced,and the results show that the middle waters of COD pollu-tion in the wetlands of Yuehai Lake were higher than those in the south and north.
Yuehai Lake wetlandLandsat-8convolutional neural networkswater quality inversionCOD
NETL
NSTL
万方数据
