Spatial characterisation of groundwater systems using fuzzy c-mean clustering: A multi-parameter approach in crystalline aquifers
Bibliographic record
Abstract
Similarity based approaches to groundwater system characterization has proven to be of great value in describing groundwater system in terms of assessment and prediction of groundwater resource where data are scarce in heterogeneous basement complex environment. With this aim, a fuzzy c-means clustering approach was utilized to co-locate hydrogeophysical parameters according to their similarities into groups, that are internally homogeneous which informs a specific hydrogeological zone. The study was carried out in some part of Osun River catchment, Ilesa southwestern Nigeria, a data-scarce area, which currently suffer water scarcity due to many wells no longer being productive. The study utilizes data from remote sensing, borehole data and geophysical data, such as Landsat image, Digital Elevation Model (DEM), borehole yield, aeromagnetic, and electrical resistivity datasets. Factors influencing spatial-temporal variations of groundwater occurrence of an aquifer in crystalline rocks such as slope, lineament density, drainage density, structural density, aquifer thickness, aquifer resistivity, overburden thickness, transverse resistivity, longitudinal resistivity, anisotropy coefficient were assessed and subjected to fuzzy c-mean algorithm. The fuzzy c-means clustering analysis identified three distinct clusters, each representing zones with similar hydrogeological properties, and some overlap exists due to mixed characteristics at some locations. The cluster validity index results suggest a good well-separated, distinguishable cluster. The findings established that the study area is characterized with three hydrogeological zones, with no definite boundaries between the zones, different from the catchment. Moreover, the study presents an approach to define and describe the target system using the groundwater yield similarity to hydrogeophysical properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".