Research output per year
Research output per year
Description
This project is part of the Saline Intrusion in Coastal Aquifers (SALINA) research project. Data acquisition and analysis was conducted in Queen’s University Belfast (2019-2020). Funding was provided by EPSRC Standard Research (Grant No. EP/R019258/1).
If you use Computer-Vision-Sandbox-Images as part of your workflow in a scientific publication, please consider citing the repository with the following DOI:
Etsias, G.; Hamill, G.A.; Benner, E.M.; Águila, J.F.; McDonnell, M.C.; Flynn, R.; Ahmed, A.A. Optimizing Laboratory Investigations of Saline Intrusion by Incorporating Machine Learning Techniques. Water 2020, 12, 2996. DOI: 10.3390/w12112996.
If you use Computer-Vision-Sandbox-Images as part of your workflow in a scientific publication, please consider citing the repository with the following DOI:
Etsias, G.; Hamill, G.A.; Benner, E.M.; Águila, J.F.; McDonnell, M.C.; Flynn, R.; Ahmed, A.A. Optimizing Laboratory Investigations of Saline Intrusion by Incorporating Machine Learning Techniques. Water 2020, 12, 2996. DOI: 10.3390/w12112996.
| Date made available | Mar 2021 |
|---|---|
| Publisher | Queen's University Belfast |
| Date of data production | 2018 - 2020 |
Research output
- 1 Article
-
Optimizing Laboratory Investigations of Saline Intrusion by Incorporating Machine Learning Techniques
Etsias, G., Hamill, G. A., Benner, E., Águila, J. F., McDonnell, M. C., Flynn, R. & Ahmed, A. A., 26 Oct 2020, In: Water. 12, 11, 21 p., 2996.Research output: Contribution to journal › Article › peer-review
Open AccessFile3 Citations (Scopus)50 Downloads (Pure)
Student theses
-
Machine learning assisted, laboratory and numerical study of saltwater intrusion in stratified and fractured coastal aquifers
Author: Etsias, G., Dec 2022Supervisor: Hamill, G. (Supervisor) & Flynn, R. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy
File