Sustainable Groundwater Management in Water-Scarce Regions: A Spatial Machine Learning Analysis from Rajshahi, Bangladesh
Bibliographic record
Abstract
Ensuring the availability and sustainable management of water (SDG 6) is particularly challenging in dry regions like Rajshahi, Bangladesh, where communities rely heavily on groundwater with limited recharge potential. Issues such as declining water levels and contamination by iron, arsenic, and chloride compromise both user satisfaction and public health. This study aimed to assess groundwater quality risks through regional mapping to guide the installation depth of new water sources. In collaboration with the Department of Public Health Engineering (DPHE), data were collected from 7,388 tube wells across nine upazilas, including well depth, geographic coordinates, and contaminant concentrations. Water quality was evaluated against World Health Organization and Bangladesh standards. Machine learning (XGBoost) and spatial analysis were applied to model contaminant levels based on location and well depth. An initial model showed poor performance, but after identifying and correcting key errors, the refined model yielded significant improvements: R² increased from 0.0345 to 0.62 for iron, from −0.0015 to 0.38 for arsenic, and from 0.12 to 0.71 for chloride. A comprehensive water quality risk map was developed by integrating these results at the upazila level. This map provides actionable insights for government agencies and NGOs to prioritize areas for water quality testing, remediation, and public awareness initiatives, contributing to more informed and sustainable water resource management in the region.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".