Application Research on Complex Water Quality Prediction with Improved QGA-BP Model

Water quality prediction is a prerequisite for planning and managing of water environment and integrated controlling of water pollution. However, the construction of mechanism models is complex, the massive computation and data are required, prediction effects are not accurate enough and the further application of mechanism models is difficult. An improved QGA-BP model is constructed for predicting water quality of complex Huaihe river. The dynamic improvement strategy and catastrophe strategy are used as evolutionary operation guidelines in QGA to optimize the weight and the threshold of BP model. The past observation data are applied as the example to train the model. The comparison of experimental results show that the evolution generation, convergence speed and prediction precision of the improved QGA-BP model are improved. The model is applicable to solve the black box problem of water prediction and provides a new way for water environment management.