Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels-Reference-Cited by-同舟云学术

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Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels

Published:2023-04-28 Issue:8 Volume:37 Page:2945-2967
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Kumar Satish,Pradhan Arpan,Khuntia Jnana Ranjan,Khatua Kishanjit Kumar

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11269-022-03409-5.pdf

Reference59 articles.

1. Amershi S et al (2019) Software engineering for machine learning: A case study. IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), Montreal, QC, Canada, pp. 291–300. https://doi.org/10.1109/ICSE-SEIP.2019.00042

2. Azamathulla HM (2012) Gene expression programming for prediction of scour depth downstream of sills. J Hydrol 460:156–159

3. Azamathulla HM, Ahmad Z (2015) Closure to “Estimation of critical velocity for slurry transport through pipeline using adaptive neuro-fuzzy interference system and gene-expression programming” by H. Md. Azamathulla and Z. Ahmad. J Pipeline Syst Eng Pract 6(1):07014004

4. Azamathulla HM, Ahmad Z, Ghani AA (2013) An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming. Neural Comput Appl 23(5):1343–1349

5. Azamathulla HM, Ghani AA (2010) ANFIS-based approach for predicting the scour depth at culvert outlets. J Pipeline Syst Eng Pract 2(1):35–40

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4. Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms;Water Science & Technology;2023-12-23

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