Water Quality Evaluation Based on RBF Neural Network with Parameters Optimized by PSO Algorithm

In order to improve water quality evaluation of multi-spectral image accurately,this paper puts forward a model for water quality evaluation based on RBF Neural Network with parameters optimized by particle swarm optimization algorithms. The model uses High-resolution multi-spectral remote SPOT-5 data and the water quality field data, chose four representative water quailty parameters, RBF Neural Network are trained and tested,the parameters of RBF Neural Network are optimized by particle swarm optimization algorithms. Finally, The proposed model is applied to the water quality evaluation of Weihe River in Shaanxi Province.The result of experiment shows the proposed method can give a better quality comprehensive evaluation, and can reflect the water quality of rivers accurately and objectively from the overall. It provides a new approach for evaluation of environment to inland rivers.