GROUNDWATER POTENTIAL MAPPING (GWPM) USING ANALYTICAL HIERARCHY PROCESS (AHP) IN BENGKULU CITY, INDONESIA
Bibliographic record
Abstract
Rapid urbanization and industrial expansion have led to heightened water demand. Groundwater plays a crucial role in urban settings for sanitation, drinking water, and agriculture. This research seeks to assess groundwater potential in Bengkulu City, Indonesia, employing the Analytical Hierarchy Process (AHP). The study incorporates eight primary factors: lineament density, drainage density, precipitation, geomorphology, geology, slope, land cover, and elevation. A multicollinearity test confirmed the absence of multicollinearity among these parameters. The pairwise comparison matrix produced a consistency ratio of 0.098 (9.8%), indicating acceptable consistency in parameter comparisons. Overlay-weighted linear combination (WLC) analysis categorized the groundwater potential into five levels: very high (2.9% or 3.83 km²), high (50.9% or 67.47 km²), moderate (35.2% or 46.71 km²), low (5.2% or 6.88 km²), and very low (5.8% or 7.64 km²). The AHP model yielded strong performance metrics, including a ROC value of 0.89, accuracy of 0.81, MAE of 0.19, RMSE of 0.43, Kappa of 0.62, precision of 0.86, recall of 0.80, and an F1-score of 0.83. Precipitation, lineament density, and drainage density were the key factors affecting groundwater potential. This study shows that the AHP method is highly effective for mapping groundwater potential, especially in urban areas like Bengkulu City. The results can assist in making informed decisions regarding well drilling for drinking water, agricultural purposes, and artificial recharge projects, contributing to sustainable groundwater management in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".