An SVM model for Water Quality Monitoring Using Remote Sensing Image

The accuracy of traditional monitoring methods using remote sensing was lower, because of the limited number of monitoring points on the Tai lake. This paper proposed to use the Least Squares Support Vector Machine (LS-SVM) theory to improve the accuracy of water quality retrieval, which is suitable for the small-sample fitting. The LS-SVM model was used to monitor concentration of suspended matter. In this paper, the Radial Basic Function (RBF) was chosen as the kernel function of the retrieval model, and the grid searching and k-cross validation were used to choose and optimize the parameters. From the results of experiment, it showed that the proposed method had good performance and at the same time, the complexity is lower and the speed of the modeling was rapid.