Grey Prediction Model for the Chemical Oxygen Demand Emissions in Industrial Waste Water: An Empirical Analysis of China

摘要

Resource conservation and ecological protection are important objectives of the industrial transformation development in China, so it is significant to improve the level of industrial waste water monitoring. In the perspective of prediction for the chemical oxygen demand emissions in industrial waste water, the GM-Verhulst-SCGM grey combined prediction model was constructed to predict chemical oxygen demand emissions based on the GM (1,1) model, Verhulst model and SCGM (1,1)c model, and mean relative error and mean absolute error were selected to compare statistical properties of the prediction models. The prediction results show that the GM-Verhulst-SCGM grey combined prediction model had higher prediction accuracy compared with single prediction model, and the lowest mean absolute error value 0.002. The future time-series data of industrial chemical oxygen demand emissions was estimated on the basis of the feasibility of grey combined prediction model was validated. So it could provide theory and method for related department with the reference of improving industrial water management level.