A Systematic Review of Groundwater Management Applied to Rural Development
Bibliographic record
Abstract
Groundwater is a key resource, and its management is vital to rural communities' development.However, inappropriate actions can affect its sustainability.This study aims to analyse the scientific literature on groundwater management in rural communities, using the Scopus database for bibliometric research analysis and a systematic literature review for identifying sustainable groundwater management strategies.The methodology comprised i) selection, processing and classification of data, ii) application of bibliometrics, and iii) a systematic review for identifying sustainable water management strategies.The bibliometric analysis contemplated 1,247 scientific documents.An exponential growth of publications in the study field since its inception (1936) is evident, highlighting scientific collaborations between China, the United States, India, Australia and France.Seven study areas stood out: agriculture and climate change, water management and geographic information systems, water quality, remote sensing, hydrochemistry in arid regions, and nitrate and hydrogeochemical pollution.Research trends in recent years include the Analytical Hierarchy Process (AHP), recharge, sustainability, drainage, and land subsidence.The systematic review of 67 documents allowed the identification of social, political, economic, environmental, and academic/technical strategies for sustainable groundwater management, highlighting four central cores to be worked on: strengthening top-level design, establishing groundwater monitoring and early warning, innovating agricultural water-saving technologies, and carrying out public education on science and technology.This research provides a vision of the strategies for the sustainability of groundwater resources in aquifer-dependent areas and highlights the key areas to develop for groundwater sustainability in a rural context, in alignment with 1, 2, 6 and 15 Sustainable Development Goals.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.035 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".