Supervised Classification of Groundwater Potential Mapping Using Integrated Machine Learning and GIS-Based Techniques
Bibliographic record
Abstract
Addressing the global water depletion challenge, this study integrates five supervised machine learning algorithms (MLAs) with GIS-based techniques to assess groundwater potential.The employed MLAs include Ensemble Boosted Trees (logic-based learners), Naive Bayes (NB; statistical learning algorithms), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP; Artificial Neural Networks), and k-Nearest Neighbors (kNN; instance-based learners).These MLAs were utilized to generate groundwater potential maps (GPMs) based on seven influential variables: aquifer unit types, transmissivity, lineament density, slope, soil type, land use/land cover, and drainage density.Classifier performance was evaluated using metrics such as True Positive Rates (TPR), False Negative Rates (FNR), Positive Predictive Values (PPV), False Discovery Rates (FDR), and the Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curves.Results indicate that kNN-based learners outperformed other methods, achieving a validation accuracy of 90.70% and an AUC of 1, which corresponds to 100% accurate predictions.Ensemble Boosted Trees, MLP, SVM, and NB followed, with validation accuracies of 89.7%, 79.4%, 77.6%, and 75.7%, respectively.The methodology developed in this study can be applied to estimate and manage potential groundwater resources in regions facing water scarcity issues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".