Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Jeung, Minhyuk; Jang, Jae Yool; Yoon, Kwang‐Sik; Baek, Sang-Soo

doi:10.1016/j.jhydrol.2023.129973

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Cited by 6 publications

References 90 publications

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Virtual special issue “Changes in hydrological processes and water resources in the context of climate change and carbon neutrality”

Lei,

Wang,

Liu

2023

Journal of Hydrology

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Virtual special issue “Changes in hydrological processes and water resources in the context of climate change and carbon neutrality”

Lei,

Wang,

Liu

2023

Journal of Hydrology

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Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model

Tian,

Luo,

Guo

2024

Journal of Water Process Engineering

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A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare

Zheng,

Hao,

Wang

et al. 2024

ACM Comput. Surv.

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Developing smart cities is vital for ensuring sustainable development and improving human well-being. One critical aspect of building smart cities is designing intelligent methods to address various decision-making problems that arise in urban areas. As machine learning techniques continue to advance rapidly, a growing body of research has been focused on utilizing these methods to achieve intelligent urban decision making. In this survey, we conduct a systematic literature review on the application of machine learning methods in urban decision making, with a focus on planning, transportation, and healthcare. First, we provide a taxonomy based on typical applications of machine learning methods for urban decision making. We then present background knowledge on these tasks and the machine learning techniques that have been adopted to solve them. Next, we examine the challenges and advantages of applying machine learning in urban decision making, including issues related to urban complexity, urban heterogeneity and computational cost. Afterward and primarily, we elaborate on the existing machine learning methods that aim to solve urban decision making tasks in planning, transportation, and healthcare, highlighting their strengths and limitations. Finally, we discuss open problems and the future directions of applying machine learning to enable intelligent urban decision making, such as developing foundation models and combining reinforcement learning algorithms with human feedback. We hope this survey can help researchers in related fields understand the recent progress made in existing works, and inspire novel applications of machine learning in smart cities.

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Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Cited by 6 publications

References 90 publications

Virtual special issue “Changes in hydrological processes and water resources in the context of climate change and carbon neutrality”

Virtual special issue “Changes in hydrological processes and water resources in the context of climate change and carbon neutrality”

Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model

A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare

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