Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network-Reference-Cited by-同舟云学术

Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network

Published:2017-06 Issue:6 Volume:53 Page:4564-4585
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Babakhani Peyman¹²^ORCID,Bridge Jonathan²³^ORCID,Doong Ruey‐an¹⁴^ORCID,Phenrat Tanapon⁵⁶^ORCID

Affiliation:

1. Department of Biomedical Engineering and Environmental SciencesNational Tsing Hua UniversityHsinchu Taiwan

2. Department of Civil Engineering and Industrial DesignUniversity of LiverpoolLiverpool UK

3. Department of the Natural and Built EnvironmentSheffield Hallam UniversitySheffield UK

4. Institute of Environmental Engineering, National Chiao Tung UniversityHsinchu Taiwan

5. Research Unit for Integrated Natural Resources Remediation and Reclamation, Department of Civil Engineering, Faculty of EngineeringNaresuan UniversityPhitsanulok Thailand

6. Center of Excellence for Sustainability of Health, Environment and Industry, Faculty of EngineeringNaresuan UniversityPhitsanulok Thailand

Funder

Taiwan's Ministry of Science and Technology (MOST)

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/2016WR020358

Reference165 articles.

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5. Modified MODFLOW-based model for simulating the agglomeration and transport of polymer-modified Fe0 nanoparticles in saturated porous media

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