Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting

作者：

Highlights：

• A novel way is proposed for daily urban water demand forecasting.

• Both data pre-processing and optimal parameter selection techniques are used.

• The results show that the proposed model is superior to some existing models.

• The proposed model can be a promising model to forecast urban water consumptions.

摘要

•A novel way is proposed for daily urban water demand forecasting.•Both data pre-processing and optimal parameter selection techniques are used.•The results show that the proposed model is superior to some existing models.•The proposed model can be a promising model to forecast urban water consumptions.

论文关键词：Urban water demand forecasting,Long short-term memory network,Discrete wavelet transform,Principal components analysis

论文评审过程：Received 21 September 2020, Revised 18 November 2020, Accepted 2 January 2021, Available online 6 January 2021, Version of Record 24 January 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114571