Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis-Reference-Cited by-同舟云学术

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Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Published:2020-07-15 Issue:12 Volume:65 Page:2022-2042
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Sharafati Ahmad¹²³^ORCID,Haji Seyed Asadollah Seyed Babak³,Motta Davide⁴,Yaseen Zaher Mundher⁵^ORCID

Affiliation:

1. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

2. Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam

3. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

4. Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne, UK

5. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2020.1786571

Reference141 articles.

1. Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure

2. Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions

3. Prediction of Suspended Sediment Load Using Data-Driven Models

4. Daily streamflow prediction using optimally pruned extreme learning machine

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