A neural network approach for short-term water demand forecasting based on a sparse autoencoder

Haidong Huang, Zhenliang Lin, Shitong Liu, Zhixiong Zhang

Open source

DOI: 10.2166/hydro.2022.089
Published: 1
Container: Journal of Hydroinformatics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.2166/hydro.2022.089,
  title = {A neural network approach for short-term water demand forecasting based on a sparse autoencoder},
  author = {Haidong Huang and Zhenliang Lin and Shitong Liu and Zhixiong Zhang},
  year = {2023},
  journal = {Journal of Hydroinformatics},
  doi = {10.2166/hydro.2022.089},
  url = {https://doi.org/10.2166/hydro.2022.089}
}

RIS

TY  - JOUR
TI  - A neural network approach for short-term water demand forecasting based on a sparse autoencoder
AU  - Haidong Huang
AU  - Zhenliang Lin
AU  - Shitong Liu
AU  - Zhixiong Zhang
PY  - 2023
JO  - Journal of Hydroinformatics
DO  - 10.2166/hydro.2022.089
UR  - https://doi.org/10.2166/hydro.2022.089
ER  -

APA

Huang, H., Lin, Z., Liu, S., & Zhang, Z. (2023). A neural network approach for short-term water demand forecasting based on a sparse autoencoder. Journal of Hydroinformatics. https://doi.org/10.2166/hydro.2022.089

Source records

doaj · retrieved 2026-09-25T02:37:09.491Z