Prediction of annual water consumption in Guangdong Province based on Bayesian neural network

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Tian, Tao^1 ; Xue, Huifeng^1

China Aerospace Academy of Systems Science and Engineering, No 16 Fucheng road, Beijing

100048, China^1

关键词: Annual water consumption;    Bayesian neural networks;    Bayesian regularization;    BP neural network model;    Forecasting modeling;    Regional water resources;    Water demand forecasts;    Water resources management;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012032/pdf
DOI  :  10.1088/1755-1315/69/1/012032

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In the context of the implementation of the most stringent water resources management system, the role of water demand forecasting for regional water resources management is becoming increasingly significant. Based on the analysis of the influencing factors of water consumption in Guangdong Province, we made the forecast index system of annual water consumption, and constructed the forecast model of annual water consumption of BP neural network, then optimized the regularization BP neural network in utilization rate of water. The results showed that the average absolute percentage error of Bayesian neural network prediction model and BP neural network prediction model is 0.70% and 0.46% respectively. BP neural network model by Bayesian regularization is more ability to improve the accuracy of about 0.24%, more in line with the regional annual water demand forecast high precision requirements. Take the planning index value of Guangdong Province's thirteen five plan into Bayesian neural network forecasting model, and its forecast value is 45.432 billion cubic meters, which will reach 456.04 billion cubic meters of red water in Guangdong Province in 2020.