GIS-based drought assessment for water-sensitive urban planning in Anantapur District
Bibliographic record
Abstract
Geographic information system (GIS) technological assessment of drought conditions is essential for water-sensitive development, especially in areas like Anantapur District in Andhra Pradesh. This semi-arid region faces severe water scarcity, worsened by weather patterns and farming methods. GIS offers a comprehensive method for assessing, visualising, and managing drought vulnerabilities, enabling targeted interventions. By evaluating the spatial distribution of drought vulnerability, GIS techniques facilitate the creation of spatial maps that highlight climate and environmental factors. The Weighted Overlay Tool of GIS generates a drought vulnerability index, incorporating variables like vegetation, soil moisture, and precipitation levels to provide a detailed understanding of drought effects. GIS modelling also identifies suitable sites for groundwater recharge by integrating successful water management practices. This study aims to enhance inter-district assistance and self-sustainability by mapping interventions to ensure efficient resource use. The results offer valuable insights to policymakers and urban planners, promoting sustainable development in Anantapur. In addition, the approach provides a replicable model for other water-scarce regions, aiding decision makers in resource allocation and infrastructure development to mitigate drought impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".