Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network-Reference-Cited by-同舟云学术

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Published:2022 Issue: Volume:10 Page:124238-124258
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nguyen Lam Van¹^ORCID,Bui Dieu Tien²,Seidu Razak¹

Affiliation:

1. Department of Ocean Operations and Civil Engineering, Faculty of Engineering, Smart Water and Environmental Engineering Group, Norwegian University of Science and Technology, Ålesund, Norway

2. Department of Business and IT, GIS Group, University of South-Eastern Norway, Bø i Telemark, Norway

Funder

Ålesund Municipality and the Norwegian University of Science and Technology (NTNU), Norway, through the Smart Water Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09954004.pdf?arnumber=9954004

Reference90 articles.

1. Prediction of Sewer Condition Grade Using Support Vector Machines

2. A comprehensive survey on support vector machine classification: Applications, challenges and trends

3. Application of support vector machine models for forecasting solar and wind energy resources: A review

4. Feature selection using bare-bones particle swarm optimization with mutual information

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