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

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

Content Provider	Scilit
Author	Tian, Tao Xue, Huifeng
Copyright Year	2017
Description	Journal: Iop Conference Series: Earth and Environmental Science 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.
Related Links	http://iopscience.iop.org/article/10.1088/1755-1315/69/1/012032/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/69/1/012032
Journal	Iop Conference Series: Earth and Environmental Science
Volume Number	69
Language	English
Publisher	IOP Publishing
Publisher Date	2017-06-13
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Water Science and Technology Water Resources Management Bp Neural Network Guangdong Province Annual Water Bayesian Neural Model By Bayesian
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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