Predicting sewer structural condition using hybrid machine learning algorithms

Article Properties
  • Language
    English
  • DOI (url)
    10.1080/1573062x.2023.2217430
  • Publication Date
    2023/05/30
  • Journal
    Urban Water Journal
  • Indian UGC (journal)
  • Refrences
    73
  • L. V. Nguyen Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology, Ålesund, NorwayDepartment of Geodesy, Hanoi University of Mining and Geology, Hanoi, Vietnam
  • S. Razak Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology, Ålesund, Norway
Cite
Nguyen, L. V., and S. Razak. “Predicting Sewer Structural Condition Using Hybrid Machine Learning Algorithms”. Urban Water Journal, vol. 20, no. 7, 2023, pp. 882-96, https://doi.org/10.1080/1573062x.2023.2217430.
Nguyen, L. V., & Razak, S. (2023). Predicting sewer structural condition using hybrid machine learning algorithms. Urban Water Journal, 20(7), 882-896. https://doi.org/10.1080/1573062x.2023.2217430
Nguyen LV, Razak S. Predicting sewer structural condition using hybrid machine learning algorithms. Urban Water Journal. 2023;20(7):882-96.
Journal Categories
Science
Biology (General)
Ecology
Technology
Environmental technology
Sanitary engineering
Technology
Hydraulic engineering
River, lake, and water-supply engineering (General)
Refrences
Title Journal Journal Categories Citations Publication Date
Title 2018
10.1007/978-3-319-05906-8_6 2014
10.1007/978-3-319-05906-8_6 2011
10.1007/978-3-319-05906-8_6 Environmental Technology
  • Geography. Anthropology. Recreation: Environmental sciences
  • Technology: Environmental technology. Sanitary engineering
  • Science: Biology (General): Ecology
 2021
10.1007/978-3-319-05906-8_6 2018