Standalone and ensemble-based machine learning techniques for particle Froude number prediction in a sewer system-Reference-Cited by-同舟云学术

Standalone and ensemble-based machine learning techniques for particle Froude number prediction in a sewer system

Published:2022-04-23 Issue:18 Volume:34 Page:15481-15497
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Shakya Deepti,Deshpande Vishal^ORCID,Agarwal Mayank,Kumar Bimlesh

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07237-x.pdf

Reference42 articles.

1. Ab Ghani AA (1993) Sediment transport in sewers. PhD thesis, Newcastle University

2. Ackers J, Butler D, May R (1996) Design of sewers to control sediment problems. Construction Industry Research and Information Association London

3. Alvarez-Hernandez EM (1990) The influence of cohesion on sediment movement in channels of circular cross-section. PhD thesis, Newcastle University

4. Arya Azar N, Ghordoyee Milan S, Kardan N (2021) Development of a hybrid ann-evolutionary algorithms models to predict the froude number in open channel flows in modeling of sediment transport. Environ Water Eng 7(1):73–87

5. Baran A, Lerch S, El Ayari M, Baran S (2021) Machine learning for total cloud cover prediction. Neural Comput Appl 33(7):2605–2620

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Performance evaluation of machine learning algorithms for the prediction of particle Froude number (Frn) using hyper-parameter optimizations techniques;Expert Systems with Applications;2024-12

2. Investigating the effect of fuel properties and environmental parameters on low-octane gasoline-like fuel spray combustion and emissions using machine learning-global sensitivity analysis method;Energy;2024-10

3. Advanced intelligence model for prediction of sediment transport rate and friction factor in alluvial channel;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-08-10

4. Efficient functioning of a sewer system: application of novel hybrid machine learning methods for the prediction of particle Froude number;Journal of Hydroinformatics;2024-07-24

5. Forecasting of time-dependent scour depth based on bagging and boosting machine learning approaches;Journal of Hydroinformatics;2024-07-17