Participant number | Short name of participant | Person months per participant |
---|---|---|
1 | SWRI | 12 |
2 | CERTH | 12.8 |
3 | MGRE | 3 |
4 | THL | 5.5 |
5 | CUT | 14 |
6 | POLIBA | 3.5 |
7 | FST | 4 |
8 | MEU | 10 |
Objectives
• To identify, test, and evaluate the core technologies for GWS simulation and forecasting
• To deliver prototypes and integrated methodologies that will be applicable to the test sites’ data
• To enhance modelling tools by integrating artificial intelligence and deep learning algorithms
• To combine modelling tools to a joint approach covering a wide range of data availability and complexity
Description of work
This WP constitutes the research core of MEDSAL, as it will evaluate a cascade of existing and new techniques/methodologies to develop state-of-the-art simulation tools of GWS assessment and forecast. These tools will be applied in suitable demo sites (selected sites from case study areas) as stand-alone tools, to evaluate their individual performance and identify any limitations and/or specifics needed, to be appropriately applied in selected combinations at WP5. The tools/methods to be used include hydrogeological and hydrogeochemical models, advanced geostatistics, and machine learning techniques. The aforementioned tools/methods will be combined in the last task of the WP; thus, integrated the stand-alone tools into holistic methodological approaches, as part of the overall MEDSAL Framework.
WPs | Tasks | Leaders | PPs Involved | Duration |
---|---|---|---|---|
T4.1 | Interoperability of Geo-Databse | CUT | CUT, CERTH, UIZ | M16-M18 |
T4.2 | Advanced Geostatistics | CUT | CUT | M18-M22 |
T4.3 | Physical-based models for groundwater flow and salinization transport | SWRI | SWRI, FST | M18-M22 |
T4.4 | Hydrogeochemical modelling of salinization in groundwater systems | MEU | MEU, SWRI, THL | M18-M22 |
T4.5 | Shallow and Deep Learning Methodologies | CERTH | CERTH | M18-M22 |
T4.6 | Integration of MEDSAL Models and Tools | CUT | CUT, CERTH, SWRI, THL, MEU | M20-M24 |