Radial Basis Function Regression (RBFR), ARRBFR models for Estimation of Particle Froude Number in Sewer Pipes Under Deposited Conditions
Author:
Affiliation:
1. Indian Institute of Technology Patna,Department of Computer Science and Engineering,Bihta,India
2. Indian Institute of Technology Patna,Department of Civil and Environmental Engineering,Bihta,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10111836/10111935/10112031.pdf?arnumber=10112031
Reference27 articles.
1. Application of novel hybrid machine learning techniques for particle Froude number estimation in sewer pipes
2. Estimating Particle Froude Number of Sewer Pipes by Boosting Machine-Learning Models
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4. Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images
5. Standalone and ensemble-based machine learning techniques for particle Froude number prediction in a sewer system
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1. 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
2. Predict Total Sediment Load Using Standalone and Ensemble Machine Learning Models;Lecture Notes in Networks and Systems;2024
3. Estimation of Particle Froude Number in Deposited Bed Condition Using Hybrid Machine Learning Models;Lecture Notes in Networks and Systems;2024
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