Research on the water quality detection method based on fluorescence spectrometry and PSO-RBF network

Abstract

In this paper, we propose a model-based method for lake water quality testing that is used to detect and analyze the content of pollutants in lake. The known and unknown contaminants in mixed solutions are detected by fluorescence spectroscopy and spectrophotometry. A PSO-RBF neural network model is established to predict the content of pollutants in the lake. The prediction results of the PSO-RBF neural network model are compared with the ones of a RBF neural network model to verify the computational efficiency and the prediction accuracy of the model. The results show that the PSO-RBF neural network could be used to predict the pollutant content of lake water quality effectively, which provides a fast and practical method for water quality testing.