Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches-Reference-Cited by-同舟云学术

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Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches

Published:2024-06 Issue: Volume:22 Page:102320
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Kumar Sanjit,Deshpande Vishal,Agarwal Mayank,Rathnayake Upaka^ORCID

Publisher

Elsevier BV

Reference44 articles.

1. Self-cleansing Drainage System Design by Incipient Motion and Incipient Deposition-Based Models;Safari,2016

2. Sediment transport over deposited beds in sewers;Nalluri;Water Sci. Technol.,1994

3. Sediment Transport in Sewers;Ghani,1993

4. Urban storm sewer design: approach in consideration of sediments;Ota;J. Hydraul. Eng.,2003

5. Design of Sewers to Control Sediment Problems;Ackers,1996

Cited by 2 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. Novel feature selection based ANN for optimal solar panels tilt angles prediction in micro grid;Case Studies in Thermal Engineering;2024-09

2. 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

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