Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields-Reference-Cited by-同舟云学术

Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields

Published:2020-03 Issue:3 Volume:56 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Arshadi Masoud¹^ORCID,De Paolis Kaluza M. Clara²^ORCID,Miller Eric L.³^ORCID,Abriola Linda M.¹^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering Tufts University Medford MA USA

2. College of Computer and Information Science Northeastern University Boston MA USA

3. Department of Electrical and Computer Engineering Tufts University Medford MA USA

Funder

Strategic Environmental Research and Development Program

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR026481

Reference113 articles.

1. Guest Editorial: Contaminant Source Zones: Remediation or Perpetual Stewardship?

2. Source Remediation Challenges

3. Abriola L. M. Rathfelder K. Maiza M. &Yadav S.(1992).VALOR code version 1.0: A PC code for simulating immiscible contaminant transport in subsurface systems. Final report (No. EPRI‐TR‐101018). Electric Power Research Inst. Palo Alto CA (United States); Michigan Univ. Ann Arbor MI (United States). Dept. of Civil and Environmental Engineering. Retrieved fromhttps://www.osti.gov/biblio/10186175

4. Joint simulation of multiple variables with a Markov-type coregionalization model

5. Flux-based assessment at a manufacturing site contaminated with trichloroethylene

Cited by 12 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. Evaluation of multiphase transport of benzene in the unsaturated zone: interactions of volatilization and dissolution through experiments and modeling;Environmental Earth Sciences;2024-09

2. Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance;Water Resources Research;2024-06-26

3. Characterization of DNAPL source zones in clay-sand media via joint inversion of DC resistivity, induced polarization and borehole data;Journal of Contaminant Hydrology;2023-09

4. Numerical Modeling and Data‐Worth Analysis for Characterizing the Architecture and Dissolution Rates of a Multicomponent DNAPL Source;Water Resources Research;2023-05

5. Comparaison des méthodes d’inversion géostatistiques et d’apprentissage profond pour la caractérisation de l’architecture de la zone source DNAPL;Hydrogeology Journal;2023-02-20