Machine learning and urban drainage systems: State-of-the-art review

Soon Ho Kwon, Joong Hoon Kim

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)

Abstract

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 ap-proaches 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.

Original languageEnglish
Article number3545
JournalWater (Switzerland)
Volume13
Issue number24
DOIs
Publication statusPublished - 2021 Dec 1

Keywords

  • Flood pattern recognition
  • Flood-inundation prediction
  • Machine learning
  • Pipe defect detection
  • Real-time operation control
  • Urban drainage systems

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Biochemistry
  • Aquatic Science
  • Water Science and Technology

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