Flooding and Overflow Mitigation Using Deep Reinforcement Learning Based on Koopman Operator of Urban Drainage Systems-Reference-Cited by-同舟云学术

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Flooding and Overflow Mitigation Using Deep Reinforcement Learning Based on Koopman Operator of Urban Drainage Systems

Published:2022-07 Issue:7 Volume:58 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Tian Wenchong¹,Liao Zhenliang¹²³^ORCID,Zhang Zhiyu¹^ORCID,Wu Hao⁴^ORCID,Xin Kunlun¹^ORCID

Affiliation:

1. College of Environmental Science and Engineering Tongji University Shanghai China

2. College of Civil Engineering and Architecture Xinjiang University Urumqi China

3. UNEP‐Tongji Institute of Environment for Sustainable Development Tongji University Shanghai China

4. School of Mathematical Sciences Tongji University Shanghai China

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR030939

Reference92 articles.

1. Stabilizability of linear time-varying systems

2. Low Impact Development practices in the context of United Nations Sustainable Development Goals: A new concept, lessons learned and challenges

3. Koopman operator dynamical models: Learning, analysis and control

4. Deep model predictive flow control with limited sensor data and online learning

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