Water quality parameter forecast based on grey neural network modeling
基于灰色神经网络建模的水质参数预测

Owing to the lack of enough monitoring data in water quality parameter prediction, combining information renewal GM(1,1) model and BP neural network model, the grey information renewal neural network model was proposed. Using the data of information renewal model sets as the training and testing samples, the problem that BP network needs a mass of samples to approach nonlinear function better was solved. Experimental results indicate that, compared to the grey information renewal model and BP model, this forecast model is superior in water quality parameter prediction.