GIS based Vulnerability Model Assessment of The Alexan-dra Jukskei Catchment in South AfricaGIS based Vulnerability Model Assessment of The Alexan-dra Jukskei Catchment in South Africa
Bibliographic record
Abstract
This article sets out to detail an investigation of groundwater management in the Juksei Catchment of South Africa through spatial mapping of key hydrological relationships, interactions, and parameters in catchments.The Department of Water Affairs (DWA) noted gaps in the implementation of the South African National Water Act 1998: article 16, including the lack of appropriate models for dealing with water quantity parameters.For this reason, this research conducted a drastic GIS-based groundwater assessment to improve groundwater monitoring system in the Juksei River basin catchment of South Africa.The methodology employed was a mixed-methods approach/design that involved the use of DRASTIC analysis, questionnaire, literature review and observations to gather information on how to help people who use the Juskei River.GIS (geographical information system) mapping was carried out using a three-parameter DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) vulnerability methodology.In addition, the developed vulnerability map was subjected to sensitivity analysis as a validation method.This approach included single-parameter sensitivity, sensitivity to map deletion, and correlation analysis of DRASTIC parameters.The findings were that approximately 5.7% (45km2) of the area in the northern part of the Juksei watershed is highly vulnerable.Approximately 53.6% (428.8 km^2) of the basin is also at high risk of groundwater contamination.This area is mainly located in the central, north-eastern, and western areas of the sub-basin.The medium and low vulnerability classes cover approximately 18.1% (144.8 km2) and 21.7% (168 km2) of the Jukskei River, respectively.The shallow groundwater of the Jukskei River belongs to a very vulnerable area.Sensitivity analysis indicated that water depth, water recharge, aquifer environment, soil, and topography were the main factors contributing to the vulnerability assessment.The conclusion is that the final vulnerability map indicates that the Juksei catchment is highly susceptible to pollution, and therefore, protective measures are needed for sustainable management of groundwater resources in the study area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".