Water quality prediction of Miju River based on PCA and GABP

This paper proposed a method of water quality prediction which combined Principlal Component Analysis(PCA) with Back-Propagation Neural Network optimized by Genetic Algorithm(GABP) in order to solve the problem of low prediction accuracy on account of multiple correlation factor in the traditional method of water quality prediction.Overcame the information redundancy by using Principlal Component Analysis,it extracted the variable component with strong influence.Three predition models for CODMn in Miju River were established respectively by using PCA-GABP neural network,BP neural network and GABP neural network on the basis of monitoring.600 groups of data were selected for learning and testing.It was approved that the model of water quality prediction based on PCA-GABP neural network could provide better accuracy.