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 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.
Highlights
Urban drainage systems (UDSs) are used in urban infrastructure to drain rainwater and/or used water from a system without causing floods
The objective of this paper is to summarize previous UDS studies pertaining to machine learning (ML)-based modeling/application based on three research categories: (1) operation, (2) management, and (3) maintenance (Section 4)
It is noteworthy that this review primarily focuses on methodologies, including the data types used, ML technology, and results obtained
Summary
Urban drainage systems (UDSs) are used in urban infrastructure to drain rainwater and/or used water from a system without causing floods. In this regard, high-dimensional simulations for UDS modeling (i.e., flood-inundation mapping) is typically performed, and two-dimensional (2D) hydrodynamic/hydraulic simulation models should be considered [1,2]. It is challenging to utilize a physically based hydrodynamic/hydraulic model (i.e., 2D model) for real-time UDS modeling. This significant limitation can be overcome by considering machine learning (ML) The function evaluations in various UDS problems (e.g., real-time flood-inundation forecasting and real-time operation control), generally conducted using 2D, demand high computational power because of the simulation times involved for rainfall-runoff and pipe network hydraulics [5,6,7,8].
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