Analysis of the Effectiveness of Fire Drone Missions at Disaster Sites: An Empirical Approach
Bibliographic record
Abstract
The use of drones in the public sector is expanding to various fields, and its effectiveness has been verified in some cases. Since its introduction to the Seoul Metropolitan Fire and Disaster Headquarters in 2016, drones have been used 1,240 times, including 405 times in disaster response. The purpose of this study is to analyze cases to determine the effectiveness of drones in operations such as searching, acquiring information, and monitoring, compared to traditional disaster response methods. In order to analyze the efficiency of the search missions, we divided the cases into vertical and horizontal searches and measured the response time of the drone compared to that of the firefighters. In terms of the information acquisition missions, the time spent on obtaining information and responding activities when the drone was deployed at building and forest fire sites were compared to missions in which the drone was not deployed. In the case of risk monitoring missions, the scope of the safety management personnel usually deployed at the site and the scope of the drone monitoring were compared. In horizontal searches, such as searching for missing persons, one drone can play the role of 100 people. In addition, drones are more than sixteen times faster than traditional methods in completing vertical searches in high-rise buildings, and 140 s faster in detecting fires in residential areas. Furthermore, it took more than an hour for 78 firefighters to locate a forest fire that broke out at night, but the drone located it in just two min. These results indicate that it is possible to use firefighter personnel more effectively and efficiently by using drones at disaster sites. To that end, more research on how to modulate the duties of firefighters while working with fire drones is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".