Machine Learning and Urban Drainage Systems: State-of-the-Art Review
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 f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/24/3545 |
| _version_ | 1797499862777856000 |
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| author | Soon Ho Kwon Joong Hoon Kim |
| author_facet | Soon Ho Kwon Joong Hoon Kim |
| author_sort | Soon Ho Kwon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:53:32Z |
| format | Article |
| id | doaj.art-c56ded106e564688be6277b79a8a758c |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T03:53:32Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-c56ded106e564688be6277b79a8a758c2023-11-23T11:00:48ZengMDPI AGWater2073-44412021-12-011324354510.3390/w13243545Machine Learning and Urban Drainage Systems: State-of-the-Art ReviewSoon Ho Kwon0Joong Hoon Kim1Future and Fusion Laboratory of Architectural, Civil and Environmental Engineering, Korea University, Anamdong, Seongbukgu, Seoul 02841, KoreaSchool of Civil, Environmental and Architectural Engineering, Korea University, Anamdong, Seongbukgu, Seoul 02841, KoreaIn 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.https://www.mdpi.com/2073-4441/13/24/3545machine learningurban drainage systemsflood-inundation predictionflood pattern recognitionpipe defect detectionreal-time operation control |
| spellingShingle | Soon Ho Kwon Joong Hoon Kim Machine Learning and Urban Drainage Systems: State-of-the-Art Review Water machine learning urban drainage systems flood-inundation prediction flood pattern recognition pipe defect detection real-time operation control |
| title | Machine Learning and Urban Drainage Systems: State-of-the-Art Review |
| title_full | Machine Learning and Urban Drainage Systems: State-of-the-Art Review |
| title_fullStr | Machine Learning and Urban Drainage Systems: State-of-the-Art Review |
| title_full_unstemmed | Machine Learning and Urban Drainage Systems: State-of-the-Art Review |
| title_short | Machine Learning and Urban Drainage Systems: State-of-the-Art Review |
| title_sort | machine learning and urban drainage systems state of the art review |
| topic | machine learning urban drainage systems flood-inundation prediction flood pattern recognition pipe defect detection real-time operation control |
| url | https://www.mdpi.com/2073-4441/13/24/3545 |
| work_keys_str_mv | AT soonhokwon machinelearningandurbandrainagesystemsstateoftheartreview AT joonghoonkim machinelearningandurbandrainagesystemsstateoftheartreview |