Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption

Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption

Short-term (e.g., hourly) urban water consumption (or demand) prediction is of great significance for the optimal operation of the intelligent water distribution pump stations. In this study, three single models (autoregressive integrated moving average (ARIMA), back-propagation (BP), support vector...

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Bibliographic Details
Main Authors: Xingpo Liu, Yiqing Zhang, Qichen Zhang
Format: Article
Language:English
Published: IWA Publishing 2022-05-01
Series:Journal of Hydroinformatics
Subjects:
autoregressive integrated moving average (arima)
back-propagation (bp) neural network
ensemble empirical mode decomposition (eemd)
short-term water consumption prediction
support vector machine (svm)
Online Access:http://jh.iwaponline.com/content/24/3/535

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http://jh.iwaponline.com/content/24/3/535

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