Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States-Reference-Cited by-同舟云学术

Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States

Published:2022-01-21 Issue:24 Volume:29 Page:35841-35861
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Tao Hai,Salih Sinan,Oudah Atheer Y.,Abba S. I.,Ameen Ameen Mohammed Salih,Awadh Salih Muhammad,Alawi Omer A.,Mostafa Reham R.,Surendran Udayar Pillai,Yaseen Zaher Mundher^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-022-18554-y.pdf

Reference75 articles.

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2. Abba SI, Linh NTT, Abdullahi J et al (2020) Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration. IEEE Access 8:157218–157237. https://doi.org/10.1109/ACCESS.2020.3017743

3. Ali M, Prasad R, Xiang Y et al (2021) Variational mode decomposition based random forest model for solar radiation forecasting: new emerging machine learning technology. Energy Rep 7:6700–6717

4. Alizadeh MJ, Ahmadyar D, Afghantoloee A (2017) Improvement on the Existing equations for predicting longitudinal dispersion coefficient. Water Resour Manag 31:1777–1794. https://doi.org/10.1007/s11269-017-1611-z

5. Alizadeh MJ, Shabani A, Kavianpour MR (2017) Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms. Int J Environ Sci Technol 14:2399–2410. https://doi.org/10.1007/s13762-017-1307-1

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