A novel deep neural network architecture for real-time water demand forecasting-Reference-Cited by-同舟云学术

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A novel deep neural network architecture for real-time water demand forecasting

Published:2021-08 Issue: Volume:599 Page:126353
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Salloom Tony,Kaynak Okyay,He Wei

Funder

Tsinghua University

USTB

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference50 articles.

1. Committee machines for hourly water demand forecasting in water supply systems;Ambrosio;Math. Prob. Eng.,2019

2. Short-term water demand forecasting using machine learning techniques;Antunes;J. Hydroinform.,2018

3. Tailoring seasonal time series models to forecast short-term water demand;Arandia;J. Water Resour. Plann. Manage.,2016

4. Dynamic forecast of daily urban water consumption using a variable-structure support vector regression model;Bai;J. Water Resour. Plann. Manage.,2015

5. Hybrid regression model for near real-time urban water demand forecasting;Brentan;J. Comput. Appl. Math.,2017

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