Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes-Reference-Cited by-同舟云学术

Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes

Published:2022-12-23 Issue: Volume: Page:
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Kumar Sanjit,Kirar Bablu,Agarwal Mayank,Deshpande Vishal^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Link

https://link.springer.com/content/pdf/10.1007/s11069-022-05786-x.pdf

Reference51 articles.

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3. Aksoy H, Unal NE, Mohammadi M et al (2017) Non-deposition self-cleansing design criteria for drainage systems. J Hydro-Environment Res 14:76–84

4. Alexander DC, Zikic D, Zhang J, Zhang H, Criminisi A (2014) Image quality transfer via random forest regression: applications in diffusion MRI. In: International conference on medical image computing and computer-assisted intervention. pp. 225–232

5. Aljazzar H, Leue S (2011) K⁎: A heuristic search algorithm for finding the k shortest paths. Artif Intell 175:2129–2154

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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. Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches;Results in Engineering;2024-06

3. Predict Total Sediment Load Using Standalone and Ensemble Machine Learning Models;Lecture Notes in Networks and Systems;2024

4. Estimation of Particle Froude Number in Deposited Bed Condition Using Hybrid Machine Learning Models;Lecture Notes in Networks and Systems;2024

5. Radial Basis Function Regression (RBFR), ARRBFR models for Estimation of Particle Froude Number in Sewer Pipes Under Deposited Conditions;2023 6th International Conference on Information Systems and Computer Networks (ISCON);2023-03-03