Hydrogeological data modelling in groundwater studies
Bibliographic record
Abstract
Managing, handling, exchanging and accessing hydrogeological information depend mainly on the applied hydrogeological data models, which differ between institutions and acrosscountries. Growing interest in hydrogeological information diffusion, combined with a need for information availability, require the convergence of hydrogeological data models. Modelconvergence makes hydrogeological information accessible to multiple institutions, universities, administration, water suppliers, and research organisations, at different levels: from the local level (on-site measurement teams), to national and international institutions dealing with water resources management. Furthermore, because hydrogeological studies are complex, they require a large variety of high-quality hydrogeological data with appropriatemetadata in clearly designed and coherent structures.To respond to the requirement of model convergence, easy information exchange and hydrogeological completeness, new data models have been developed, using two different methodologies. At local-regional level, the HydroCube model has been developed for the Walloon Region in Belgium. This logical data model uses entity-relationship diagrams and ithas been implemented in the MS Access environment, further enriched with a fully functional user-interface. The HydroCube model presents an innovative holistic “project-based” approach, which covers a full set of hydrogeological concepts and features, allowing for effective hydrogeological project management. This approach enables to store data about theproject localisation, hydrogeological equipment, related observations and measurements. Furthermore, topological relationships facilitate management of spatially associated data. Finally, the model focuses on specialized hydrogeological field experiments, such as pumping tests and tracer tests.At the international level, a new hydrogeological data model has been developed which guarantees hydrogeological information availability in one standard format in the scope of the FP6 project GABARDINE (“Groundwater Artificial recharge Based on Alternative sources of wateR: aDvanced Integrated technologies and management”). The model has beenimplemented in the ArcGIS environment, as a Geospatial Database for a decision support system. The GABARDINE Geospatial Database uses advantages of object-oriented modelling (UML), it follows standards for geoscientific information exchange (ISO/TC211 and OGC), and it is compliant with the recommendations from the European Geospatial Information Working Group.Finally, these two developed models have been tested with hydrogeological field data on different informatics platforms: from MS Access, through a proprietary ArcGIS environment, to the open source, free Web2GIS on-line application. They have also contributed to the development of the GroundWater Markup Language (GWML) Canadian exchange standard, compliant with Geographic Markup Language (GML). GWML has the potential of becoming an international HydroGeology Markup Language (HgML) standard with a strong and continuous support from the hydrogeological community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".