Locational planning for emergency management and response: an artificial intelligence approach
Bibliographic record
Abstract
The effi ciency of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely and effective manner upon an event's occurrence. When dealing with public sector institutions, this refl ects the signifi cance for state or local offi cials to determine the optimal locations for emergency stations and vehicles. The typical methodology to deal with such a task is through the application of the appropriate location-allocation model. In such a case, however, the spatial distribution of demand although stochastic in nature and layout, when aggregated at the appropriate level, appears to be spatially structured or semistructured. Aiming to exploit the above incentive, a different approach will be examined in this paper. The spatial tracing and location analysis of emergency incidents is achieved through the utilisation of an Artificial Neural Network (ANN). More specifi cally, the ANN provides the basis for a spatiotemporal clustering of demand, defi nition of the relevant centres, formulation of possible future states of the system and fi nally, defi nition of locational strategies for the improvement of the provided services. The proposed methodological approach is applied to Athens Metropolitan Area and the adopted dataset constitutes of the incidents that were reported and confronted by the city's Fire Department during the year 2008.
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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 | 0.001 |
| 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".