Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models-Reference-Cited by-同舟云学术

Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models

Published:2023-02-17 Issue:9 Volume:56 Page:10067-10098
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Shakya Deepti,Deshpande Vishal^ORCID,Kumar Bimlesh,Agarwal Mayank

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-023-10422-6.pdf

Reference55 articles.

1. Acaroglu ER, Graf WH (1968) Sediment transport in conveyance systems (part 2) the modes of sediment transport and their related bed forms in conveyance systems. Hydrol Sci J 13:123–135

2. Ackers P, White WR (1973) Sediment transport: new approach and analysis. J Hydraul Div 99:2041–2060

3. Brownlie W (1981a) Compilation of alluvial channel data: laboratory and field, WM Keck laboratory of hydraulics and water resources. Division of Engineering and Applied Science, California Institute of Technology, Pasadema, USA

4. Brownlie WR (1981b) Prediction of flow depth and sediment discharge in open channels

5. Chang C, Azamathulla HM, Zakaria N, Ab Ghani A (2012) Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers. J Earth Syst Sci 121:125–133

Cited by 10 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. Advanced intelligence model for prediction of sediment transport rate and friction factor in alluvial channel;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-08-10

2. Toward coupling of nonlinear support vector regression and crowd intelligence optimization algorithms in estimation of suspended sediment load;Applied Water Science;2024-08-09

3. Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability;Environmental Science and Pollution Research;2024-07-11

4. Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?;Engineering Applications of Computational Fluid Mechanics;2024-05-10

5. Comparative study of different estimation approaches to quantify suspended sediment transport at hydrometric stations: case of the wadi Mina basin, Northwest Algeria;International Journal of Energy and Water Resources;2024-05-09