A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels | Citedby Results | Journal of Hydroinformatics | IWA Publishing

Skip to Main Content

Skip Nav Destination

A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels

Journal of Hydroinformatics (2023) 25 (6): 2500–2521.

This article has been cited by the following articles in journals that are participating in CrossRef Cited-by Linking.

Machine learning rather than trial and error to close morphodynamical tuneable parameters: Application to a two-phase/two-layer model

Robin Meurice
Sandra Soares-Frazão

Journal of Hydroinformatics (2024) 26 (4): 744.

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Chengshuai Liu
Caihong Hu
Chenchen Zhao
Yue Sun
Tianning Xie
Huiliang Wang

Water Resources Management (2024) 38 (6): 2059.

Prognosis of flow of fly ash and blast furnace slag-based concrete: leveraging advanced machine learning algorithms

Rahul Kumar
Ayush Rathore
Rajwinder Singh
Ajaz Ahmad Mir
Rupesh Kumar Tipu
Mahesh Patel

Asian Journal of Civil Engineering (2024) 25 (3): 2483.

A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete

Somanshi Aggarwal
Rajwinder Singh
Ayush Rathore
Kanish Kapoor
Mahesh Patel

Materials Today Communications (2024) 39: 109294.

Optimizing seasonal discharge predictions: a hybridized approach with AI and non-linear models

Shailza Sharma
Mahesh Patel

Multiscale and Multidisciplinary Modeling, Experiments and Design (2024) 7 (4): 3405.