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
Due to the continuous development of the economy, growing human activities increase the potential to pollute waterways and degrade the environment
The results showed that regional integration could significantly reduce cross-border water pollution
Through comparative analysis of Deformable grey multivariable convolution (DGMC)(1, 2), GM(1, 2) and GM(1, 1) models, it was concluded that a DGMC(1,2) model which was able to analyze and predict the water quality of the Zhanghe River from 2013 to 2022, using chemical oxygen demand (COD) as the indicator of water quality, to a high level of accuracy
Summary
Due to the continuous development of the economy, growing human activities increase the potential to pollute waterways and degrade the environment. Cullis et al (2019) discuss the increasing risks to water quality in the Begg River basin in South Africa as a result of climate change and rapid urban development, as well as the direct and indirect economic impacts this may have on the agricultural.
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