Prediction of water consumption in Beijing based on the multi-variable grey model with adjacent accumulation

Abstract

With the rapid development of the social economy, the importance of water resources is becoming increasingly prominent. Urban water demand in Beijing has been growing rapidly. Accurate water consumption forecasting is of utmost importance for reasonable allocation and optimization of water supply systems. In this study, an innovative multi-variable grey prediction model with adjacent accumulation (AOGM(1,N)) is proposed to predict Beijing's annual water consumption for four different water usage scenarios (domestic water, agricultural water, industrial water, and environmental water) by incorporating the adjacent accumulation into the optimized grey model. Grey relational analysis is used to select the key influence factors. The adjustable parameter of the prediction model is chosen by using the particle swarm optimization algorithm. By comparing with other models in the existing literature, the proposed AOGM(1,N) model has evidently superior prediction performance based on the error indicators, which supports the novel method's merits and validity. This study could help us better understand water usage and be applied to the planning and management problems of urban water supply systems. HIGHLIGHTS An improved multi-variable grey model is proposed for water consumption prediction.; Adjacent accumulation is introduced into the proposed prediction model.; The effects of different external factors on water consumption are quantified.; The optimization method of adjustable parameter in the model is proposed.;

Keywords

• adjacent accumulation
• different water usage scenarios
• multi-variable grey model
• particle swarm optimization
• water consumption prediction