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Review

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

1
Future and Fusion Laboratory of Architectural, Civil and Environmental Engineering, Korea University, Anamdong, Seongbukgu, Seoul 02841, Korea
2
School of Civil, Environmental and Architectural Engineering, Korea University, Anamdong, Seongbukgu, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Zheng Duan
Water 2021, 13(24), 3545; https://doi.org/10.3390/w13243545
Received: 30 September 2021 / Revised: 27 November 2021 / Accepted: 8 December 2021 / Published: 11 December 2021
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies. View Full-Text
Keywords: machine learning; urban drainage systems; flood-inundation prediction; flood pattern recognition; pipe defect detection; real-time operation control machine learning; urban drainage systems; flood-inundation prediction; flood pattern recognition; pipe defect detection; real-time operation control
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MDPI and ACS Style

Kwon, S.H.; Kim, J.H. Machine Learning and Urban Drainage Systems: State-of-the-Art Review. Water 2021, 13, 3545. https://doi.org/10.3390/w13243545

AMA Style

Kwon SH, Kim JH. Machine Learning and Urban Drainage Systems: State-of-the-Art Review. Water. 2021; 13(24):3545. https://doi.org/10.3390/w13243545

Chicago/Turabian Style

Kwon, Soon H., and Joong H. Kim. 2021. "Machine Learning and Urban Drainage Systems: State-of-the-Art Review" Water 13, no. 24: 3545. https://doi.org/10.3390/w13243545

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