A comparison of machine learning models for suspended sediment load classification-Reference-Cited by-同舟云学术

A comparison of machine learning models for suspended sediment load classification

Published:2022-05-24 Issue:1 Volume:16 Page:1211-1232
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

AlDahoul Nouar¹^ORCID,Ahmed Ali Najah²^ORCID,Allawi Mohammed Falah³,Sherif Mohsen⁴⁵^ORCID,Sefelnasr Ahmed⁵^ORCID,Chau Kwok-wing⁶^ORCID,El-Shafie Ahmed⁷^ORCID

Affiliation:

1. Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

2. Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia

3. Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi, Iraq

4. Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, Al Ain, United Arab Emirates

5. National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates

6. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong

7. Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, Malaysia

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2022.2073565

Reference68 articles.

1. Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

2. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

3. ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

4. Harmonize input selection for sediment transport prediction

5. A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

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