Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China

Abstract

In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using chemical oxygen demand (COD) as the water quality indicator). HIGHLIGHTS The proposed model has the highest prediction accuracy.; The water quality in Zhanghe is predicted considering the socio-economic development.; The proposed model can be used in other rivers.;

Keywords

• chemical oxygen demand
• deformable grey multivariable convolution
• primary industry
• secondary industry
• tertiary industry
• zhanghe river