An integrated geographic information system (GIS) and analytical hierarchy process (AHP)-based approach for drone-optimized large-scale flood imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drones are a valuable tool in flood response, providing high-resolution data and real-time monitoring capabilities. However, their limited range, swath, and battery life make it challenging to cover extensive flood-prone areas. This study addresses these limitations by introducing the drone-optimized flood risk map (DOFRM) framework, integrating drones with geographic information systems (GIS) and multi-criteria decision model to prioritize survey areas. The approach leverages analytical hierarchy process (AHP) to rank high-priority zones for effective drone survey and disaster response. The study evaluated 178 drones to identify an optimal survey grid size of 1.2 × 1.2 km for efficient drone operation. This grid was placed over a flood risk map, which is a combination of various hazard and vulnerability factors, with each factor given a weight based on AHP criteria. DOFRM revealed that 17% of the region was highly susceptible to flooding. This high-risk area was further divided based on the critical regions: urban areas (3%), active channels (5%), roads (6%), rail networks (1%), stream networks (3%), and populated areas (9%). DOFRM was perceived effective and easier to use through technology acceptance model-based stakeholder’s survey. The framework enables prioritized drone deployment during large-scale flood events by optimizing resources for rapid assessment of vulnerable areas. By combining AHP-based prioritization with a GIS-based drone-optimized grid, the approach offers a systematic solution for flood risk mapping and disaster mitigation. This innovative framework enhances targeted flood surveys, enabling drone operations more effective and responsive to surveying needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it