Water Quality Prediction of Changjiang of Jingdezhen through Particle Swarm Optimization Algorithm

In order to obtain the water quality trend of Changjiang of Jingdezhen and prevent water pollution events, a water quality prediction model is built. Water quality index data, which are observed from a section of Changjiang, are taken as training samples. And eight indexes are selected, such as PH, chloride, sulfate, dissolved oxygen (DO), ammonia, permanganate, iron, total phosphorus (TP). Radial basis function (RBF) neural network model is optimized by particle swarm optimization (PSO) algorithm, and then the model is used to predict water quality indexes of Changjiang. The experimental results indicate that the deviation of predicted values calculated by the model and observed values are almost less then 4%, and we could infer that RBF neural network model optimized by PSO is a reliable and effective method for water quality prediction.