Groundwater Exploration Using Multi-Criteria Decision Analysis Method and Analytical Process in the Muda River Basin, Kedah, Malaysia
Bibliographic record
Abstract
Groundwater serves as a critical freshwater resource in the Muda River Basin, Kedah, Malaysia, where increasing demand has exceeded the capacity of the surface water supply. This study delineates Groundwater Potential Zones (GWPZs) using a Geographic Information System (GIS) integrated with Multi-Criteria Decision Analysis (MCDA). Eight geoenvironmental factors─slope, lithology, drainage density, lineament density, soil media, soil thickness, rainfall, and land use─were selected as decision criteria. The Analytic Hierarchy Process (AHP) was employed to assign relative weights to each factor, ensuring consistency ratio (CR < 0.1). The weighted thematic maps were overlaid using a weighted linear combination in GIS to generate a groundwater potential index. The resulting map was classified into five zones, ranging from very low to very high potential. Validation against discharge data from 22 wells yielded an accuracy of 77%, confirming the model’s reliability. Results indicate that lithology, rainfall, and slope are the most influential parameters, with moderate-to-high potential zones covering nearly half of the basin. This integrated approach demonstrates that GIS-based AHP is a robust, cost-effective tool for groundwater exploration and sustainable water resource management under tropical monsoon conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".