Assessing the effectiveness and the economic impact of evacuation: the case of the island of Vulcano, Italy
Bibliographic record
Abstract
Abstract. Evacuation planning and management represent a key aspect of volcanic crises because they can increase people's protection as well as minimize potential impacts on the economy, properties and infrastructure of the affected area. We present an agent-based simulation tool that assesses the effectiveness of different evacuation scenarios using the small island of Vulcano (southern Italy) as a case study. Simulation results show that the overall time needed to evacuate people should be analysed together with the percentage of people evacuated as a function of time and that a simultaneous evacuation on Vulcano is more efficient than a staged evacuation. For example, during the touristic (high) season between July and August, even though the overall duration is similar for both evacuation strategies, after ∼ 6 h about 96 % of people would be evacuated with a simultaneous evacuation, while only 86 % would be evacuated with a staged evacuation. We also present a model to assess the economic impact of evacuation as a function of evacuation duration and of the starting period with respect to the touristic season. It reveals that if an evacuation lasting 3 to 6 months was initiated at the beginning or at the end of the touristic season (i.e. June or November), it would cause a very different economic impact on the tourism industry (about 78 %–88 % and 2 %–7 % of the total annual turnover, respectively). Our results show how the assessment of evacuation scenarios that consider human and economic impact carried out in a pre-disaster context helps authorities develop evacuation plans and make informed decisions outside the highly stressful time period that characterizes crises.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".