Short-Term Water Demand Forecast Based on Deep Learning Method-Reference-Cited by-同舟云学术

Short-Term Water Demand Forecast Based on Deep Learning Method

Published:2018-12 Issue:12 Volume:144 Page:04018076
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Guo Guancheng¹,Liu Shuming²,Wu Yipeng³,Li Junyu³,Zhou Ren⁴,Zhu Xiaoyun⁴

Affiliation:

1. M.Sc. Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China.

2. Associate Professor, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China; Director, Joint Research Center for Advanced Water Technology, School of Environment-Xingrong Environmental Holding Ltd., Tsinghua Univ., Beijing 100084, China (corresponding author).

3. Ph.D. Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China.

4. Senior Engineer, Changzhou CGE Water Co., Ltd., Ju Qian Rd., Jiangsu 213003, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0000992

Reference42 articles.

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3. Al-Mutaz I. A. H. Ajbar and E. Ali. 2011. “Long term water demand forecast for the city of Riyadh Saudi Arabia.” In Proc. Int. Conf. on Water Energy and the Environment. Sharjah UAE: American Univ. of Sharjah.

4. Tailoring Seasonal Time Series Models to Forecast Short-Term Water Demand

5. Flow control by prediction of water demand

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