A grey theory based back propagation neural network model for forecasting urban water consumption

Forecasting urban water consumption is a complicated task due to its unavoidable huge fluctuation caused by uncertain factors. Back propagation neural network (BPNN) is known for its strong ability to deal with nonlinear problems but is limited by the requirement for large samples and relatively hig...

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Bibliographic Details
Published inProceedings of the 33rd Chinese Control Conference pp. 7654 - 7659
Main Authors Weiwen Wang, Junyang Jiang, Minglei Fu
Format Conference Proceeding
LanguageEnglish
Published TCCT, CAA 01.07.2014
Subjects
accumulated generating operation
Back propagation neural network
Computational modeling
Data models
Forecasting
grey theory
Neural networks
Numerical models
Predictive models
Training
urban water consumption forecasting
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Summary:Forecasting urban water consumption is a complicated task due to its unavoidable huge fluctuation caused by uncertain factors. Back propagation neural network (BPNN) is known for its strong ability to deal with nonlinear problems but is limited by the requirement for large samples and relatively high computation complexity, while grey theory has advantages such as requiring little samples and easy modeling and computing. Therefore, a combined grey theory and BPNN model named GM-BPNN is proposed is proposed to forecast urban water consumption in Hangzhou. Simulation results show that GM-BPNN can reduce the value of mean absolute percentage error (MAPE) by 6.25% and 4.62% compared with GM (1,1) and original BPNN which means GM-BPNN achieves higher prediction accuracy.
ISSN:2161-2927
DOI:10.1109/ChiCC.2014.6896276